L145minimum description learning

The candidates are:
  - predicts yes if: theory2, colour and green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predicts yes if: theory1, shape and sphere
  - predicts yes if: theory2, shape and sphere
  - predicts yes if: theory3, colour and green
  - predicts yes if: theory3, colour and red
  - predicts yes if: theory0, shape and sphere
Costs: exception cost 4.
The data are:
  - item: i4, blue, sphere and no
  - item: i0, blue, sphere and no
  - item: i3, blue, sphere and no
  - item: i5, green, cube and no
  - item: i1, red, sphere and no
  - item: i2, green, sphere and yes
  - item: i6, green, cube and no
  - item: i7, blue, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - predice sí si: theory2, es de color y verde
  - theory0 el tamaño de la teoría 2
  - theory1 el tamaño de la teoría 2
  - theory3 el tamaño de la teoría 4
  - theory2 el tamaño de la teoría 4
  - predice sí si: theory1, forma y esfera
  - predice sí si: theory2, forma y esfera
  - predice sí si: theory3, es de color y verde
  - predice sí si: theory3, es de color y rojo
  - predice sí si: theory0, forma y esfera
Costes: exception el coste: 4.
Los datos son:
  - el elemento: i4, azul, esfera y no
  - el elemento: i0, azul, esfera y no
  - el elemento: i3, azul, esfera y no
  - el elemento: i5, verde, cubo y no
  - el elemento: i1, rojo, esfera y no
  - el elemento: i2, verde, esfera y sí
  - el elemento: i6, verde, cubo y no
  - el elemento: i7, azul, cubo y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - 预测是若：theory2、颜色和绿色的
  - theory0的理论规模是2
  - theory1的理论规模是2
  - theory3的理论规模是4
  - theory2的理论规模是4
  - 预测是若：theory1、形状和球体
  - 预测是若：theory2、形状和球体
  - 预测是若：theory3、颜色和绿色的
  - 预测是若：theory3、颜色和红色的
  - 预测是若：theory0、形状和球体
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i4、蓝色的、球体和否
  - 的物品是：i0、蓝色的、球体和否
  - 的物品是：i3、蓝色的、球体和否
  - 的物品是：i5、绿色的、立方体和否
  - 的物品是：i1、红色的、球体和否
  - 的物品是：i2、绿色的、球体和是
  - 的物品是：i6、绿色的、立方体和否
  - 的物品是：i7、蓝色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - öngörür evet eğer: theory2, renk ve yeşil
  - theory0 2 kuram boyut
  - theory1 2 kuram boyut
  - theory3 4 kuram boyut
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory1, şekil ve küre
  - öngörür evet eğer: theory2, şekil ve küre
  - öngörür evet eğer: theory3, renk ve yeşil
  - öngörür evet eğer: theory3, renk ve kırmızı
  - öngörür evet eğer: theory0, şekil ve küre
Cost model: exception maliyet: 4.
Veriler:
  - item: i4, mavi, küre ve hayır
  - item: i0, mavi, küre ve hayır
  - item: i3, mavi, küre ve hayır
  - item: i5, yeşil, küp ve hayır
  - item: i1, kırmızı, küre ve hayır
  - item: i2, yeşil, küre ve evet
  - item: i6, yeşil, küp ve hayır
  - item: i7, mavi, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na kijani
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict ndiyo kama: theory1, umbo na tufe
  - predict ndiyo kama: theory2, umbo na tufe
  - predict ndiyo kama: theory3, rangi na kijani
  - predict ndiyo kama: theory3, rangi na ekundu
  - predict ndiyo kama: theory0, umbo na tufe
Cost model: exception gharimu: 4.
Data:
  - item: i4, buluu, tufe na hapana
  - item: i0, buluu, tufe na hapana
  - item: i3, buluu, tufe na hapana
  - item: i5, kijani, mchemraba na hapana
  - item: i1, ekundu, tufe na hapana
  - item: i2, kijani, tufe na ndiyo
  - item: i6, kijani, mchemraba na hapana
  - item: i7, buluu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - predicts yes if: theory2, colour and green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predicts yes if: theory1, shape and sphere
  - predicts yes if: theory2, shape and sphere
  - predicts yes if: theory3, colour and green
  - predicts yes if: theory3, colour and red
  - predicts yes if: theory0, shape and sphere
Costs: exception cost 4.
The data are:
  - item: i4, blue, sphere and no
  - item: i0, blue, sphere and no
  - item: i3, blue, sphere and no
  - item: i5, green, cube and no
  - item: i1, red, sphere and no
  - item: i2, green, sphere and yes
  - item: i6, green, cube and no
  - item: i7, blue, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(predicts_yes_if theory2 color green) (theory_size theory0 2) (theory_size theory1 2) (theory_size theory3 4) (theory_size theory2 4) (predicts_yes_if theory1 shape sphere) (predicts_yes_if theory2 shape sphere) (predicts_yes_if theory3 color green) (predicts_yes_if theory3 color red) (predicts_yes_if theory0 shape sphere)], cost_model: [(exception_cost 4)], data: [(item i4 blue sphere no) (item i0 blue sphere no) (item i3 blue sphere no) (item i5 green cube no) (item i1 red sphere no) (item i2 green sphere yes) (item i6 green cube no) (item i7 blue cube yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - vorhersagen ja wenn: theory2, Farbe und grün
  - theory0 Theorie Größe 2
  - theory1 Theorie Größe 2
  - theory3 Theorie Größe 4
  - theory2 Theorie Größe 4
  - vorhersagen ja wenn: theory1, Form und Kugel
  - vorhersagen ja wenn: theory2, Form und Kugel
  - vorhersagen ja wenn: theory3, Farbe und grün
  - vorhersagen ja wenn: theory3, Farbe und rot
  - vorhersagen ja wenn: theory0, Form und Kugel
Kosten: Ausnahme kosten 4.
Die Daten sind:
  - Ding: i4, blau, Kugel und nein
  - Ding: i0, blau, Kugel und nein
  - Ding: i3, blau, Kugel und nein
  - Ding: i5, grün, Kubus und nein
  - Ding: i1, rot, Kugel und nein
  - Ding: i2, grün, Kugel und ja
  - Ding: i6, grün, Kubus und nein
  - Ding: i7, blau, Kubus und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - predvidjeti да ȁko: theory2, бо̀ја и зѐлен
  - theory0 тѐо̄рија величѝна 2
  - theory1 тѐо̄рија величѝна 2
  - theory3 тѐо̄рија величѝна 4
  - theory2 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory1, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory2, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory3, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory3, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory0, о̏блӣк и сфе́ра
Trošak мо̀дел: изузе́так ко̏штати 4.
Податци:
  - item: i4, пла̑в, сфе́ра и no
  - item: i0, пла̑в, сфе́ра и no
  - item: i3, пла̑в, сфе́ра и no
  - item: i5, зѐлен, коцка и no
  - item: i1, цр̀вен, сфе́ра и no
  - item: i2, зѐлен, сфе́ра и да
  - item: i6, зѐлен, коцка и no
  - item: i7, пла̑в, коцка и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - ennakoida kyllä jos: theory2, väri ja vihreä
  - theory0 teoria koko 2
  - theory1 teoria koko 2
  - theory3 teoria koko 4
  - theory2 teoria koko 4
  - ennakoida kyllä jos: theory1, muoto ja pallo
  - ennakoida kyllä jos: theory2, muoto ja pallo
  - ennakoida kyllä jos: theory3, väri ja vihreä
  - ennakoida kyllä jos: theory3, väri ja punainen
  - ennakoida kyllä jos: theory0, muoto ja pallo
Kustannus malli: poikkeus maksaa 4.
Tieto:
  - esine: i4, sininen, pallo ja ei
  - esine: i0, sininen, pallo ja ei
  - esine: i3, sininen, pallo ja ei
  - esine: i5, vihreä, kuutio ja ei
  - esine: i1, punainen, pallo ja ei
  - esine: i2, vihreä, pallo ja kyllä
  - esine: i6, vihreä, kuutio ja ei
  - esine: i7, sininen, kuutio ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - 预测是若：theory2、颜色和绿色的
  - theory0的理论规模是2
  - theory1的理论规模是2
  - theory3的理论规模是4
  - theory2的理论规模是4
  - 预测是若：theory1、形状和球体
  - 预测是若：theory2、形状和球体
  - 预测是若：theory3、颜色和绿色的
  - 预测是若：theory3、颜色和红色的
  - 预测是若：theory0、形状和球体
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i4、蓝色的、球体和否
  - 的物品是：i0、蓝色的、球体和否
  - 的物品是：i3、蓝色的、球体和否
  - 的物品是：i5、绿色的、立方体和否
  - 的物品是：i1、红色的、球体和否
  - 的物品是：i2、绿色的、球体和是
  - 的物品是：i6、绿色的、立方体和否
  - 的物品是：i7、蓝色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - 予言するはいもし...:theory2, 色と緑の
  - theory02理論大きさ
  - theory12理論大きさ
  - theory34理論大きさ
  - theory24理論大きさ
  - 予言するはいもし...:theory1, 形と球
  - 予言するはいもし...:theory2, 形と球
  - 予言するはいもし...:theory3, 色と緑の
  - 予言するはいもし...:theory3, 色と赤い
  - 予言するはいもし...:theory0, 形と球
費用モデル:例外費やす:4.
資料:
  - 項目:i4, 青い, 球といいえ
  - 項目:i0, 青い, 球といいえ
  - 項目:i3, 青い, 球といいえ
  - 項目:i5, 緑の, 立方体といいえ
  - 項目:i1, 赤い, 球といいえ
  - 項目:i2, 緑の, 球とはい
  - 項目:i6, 緑の, 立方体といいえ
  - 項目:i7, 青い, 立方体とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - öngörür evet eğer: theory2, renk ve yeşil
  - theory0 2 kuram boyut
  - theory1 2 kuram boyut
  - theory3 4 kuram boyut
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory1, şekil ve küre
  - öngörür evet eğer: theory2, şekil ve küre
  - öngörür evet eğer: theory3, renk ve yeşil
  - öngörür evet eğer: theory3, renk ve kırmızı
  - öngörür evet eğer: theory0, şekil ve küre
Cost model: exception maliyet: 4.
Veriler:
  - item: i4, mavi, küre ve hayır
  - item: i0, mavi, küre ve hayır
  - item: i3, mavi, küre ve hayır
  - item: i5, yeşil, küp ve hayır
  - item: i1, kırmızı, küre ve hayır
  - item: i2, yeşil, küre ve evet
  - item: i6, yeşil, küp ve hayır
  - item: i7, mavi, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory0 2
  - نَظَرِيَّة مَقَاس theory1 2
  - نَظَرِيَّة مَقَاس theory3 4
  - نَظَرِيَّة مَقَاس theory2 4
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory0، شَكْل وَـ كُرَة
cost model: اِسْتِثْنَاء cost 4.
بَيَانَات:
  - بَنْد: i4، أَزْرَق، كُرَة وَـ no
  - بَنْد: i0، أَزْرَق، كُرَة وَـ no
  - بَنْد: i3، أَزْرَق، كُرَة وَـ no
  - بَنْد: i5، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i1، أَحْمَر، كُرَة وَـ no
  - بَنْد: i2، أَخْضَر، كُرَة وَـ نَعَم
  - بَنْد: i6، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i7، أَزْرَق، كَعْبَة وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - predict დიახ თუ: theory2, ფერი და მწვანე
  - theory0 2 თეორია ზომა
  - theory1 2 თეორია ზომა
  - theory3 4 თეორია ზომა
  - theory2 4 თეორია ზომა
  - predict დიახ თუ: theory1, ფორმა და სფერო
  - predict დიახ თუ: theory2, ფორმა და სფერო
  - predict დიახ თუ: theory3, ფერი და მწვანე
  - predict დიახ თუ: theory3, ფერი და წითელი
  - predict დიახ თუ: theory0, ფორმა და სფერო
Ფასი მოდელი: გამონაკლისი ღირს 4.
Მონაცემები:
  - item: i4, ლურჯი, სფერო და no
  - item: i0, ლურჯი, სფერო და no
  - item: i3, ლურჯი, სფერო და no
  - item: i5, მწვანე, კუბი და no
  - item: i1, წითელი, სფერო და no
  - item: i2, მწვანე, სფერო და დიახ
  - item: i6, მწვანე, კუბი და no
  - item: i7, ლურჯი, კუბი და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - matakite āe mēnā: theory2, kano me kākāriki
  - ariā size theory0 2
  - ariā size theory1 2
  - ariā size theory3 4
  - ariā size theory2 4
  - matakite āe mēnā: theory1, āhuahanga me poi
  - matakite āe mēnā: theory2, āhuahanga me poi
  - matakite āe mēnā: theory3, kano me kākāriki
  - matakite āe mēnā: theory3, kano me red
  - matakite āe mēnā: theory0, āhuahanga me poi
Cost model: hāungatanga cost 4.
Hōtuku:
  - tūemi: i4, kahurangi, poi me kāo
  - tūemi: i0, kahurangi, poi me kāo
  - tūemi: i3, kahurangi, poi me kāo
  - tūemi: i5, kākāriki, mataono me kāo
  - tūemi: i1, red, poi me kāo
  - tūemi: i2, kākāriki, poi me āe
  - tūemi: i6, kākāriki, mataono me kāo
  - tūemi: i7, kahurangi, mataono me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - dự báo vâng nếu: theory2, màu và green
  - theory0 lý thuyết kích thước 2
  - theory1 lý thuyết kích thước 2
  - theory3 lý thuyết kích thước 4
  - theory2 lý thuyết kích thước 4
  - dự báo vâng nếu: theory1, hình dáng và mặt cầu
  - dự báo vâng nếu: theory2, hình dáng và mặt cầu
  - dự báo vâng nếu: theory3, màu và green
  - dự báo vâng nếu: theory3, màu và đỏ
  - dự báo vâng nếu: theory0, hình dáng và mặt cầu
Cost model: lệ ngoại cost 4.
Dữ liệu:
  - vật phẩm: i4, blue, mặt cầu và no
  - vật phẩm: i0, blue, mặt cầu và no
  - vật phẩm: i3, blue, mặt cầu và no
  - vật phẩm: i5, green, lập phương và no
  - vật phẩm: i1, đỏ, mặt cầu và no
  - vật phẩm: i2, green, mặt cầu và vâng
  - vật phẩm: i6, green, lập phương và no
  - vật phẩm: i7, blue, lập phương và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - ทำนายใช่ถ้า หาก:theory2, สีและเขียว
  - theory0ทฤษฎีขนาด2
  - theory1ทฤษฎีขนาด2
  - theory3ทฤษฎีขนาด4
  - theory2ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory3, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory3, สีและแดง
  - ทำนายใช่ถ้า หาก:theory0, รูปร่างและทรงกลม
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:4.
ข้อมูล:
  - item:i4, น้ำเงิน, ทรงกลมและไฮ้
  - item:i0, น้ำเงิน, ทรงกลมและไฮ้
  - item:i3, น้ำเงิน, ทรงกลมและไฮ้
  - item:i5, เขียว, ลูกบาศก์และไฮ้
  - item:i1, แดง, ทรงกลมและไฮ้
  - item:i2, เขียว, ทรงกลมและใช่
  - item:i6, เขียว, ลูกบาศก์และไฮ้
  - item:i7, น้ำเงิน, ลูกบาศก์และใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் பச்சை
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - கணி ஆம் என்றால்: theory1, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory2, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory0, shape மற்றும் sphere
cost model: exception செலவு 4.
தரவுகள்:
  - item: i4, நீலம், sphere மற்றும் no
  - item: i0, நீலம், sphere மற்றும் no
  - item: i3, நீலம், sphere மற்றும் no
  - item: i5, பச்சை, cube மற்றும் no
  - item: i1, சிவப்பு, sphere மற்றும் no
  - item: i2, பச்சை, sphere மற்றும் ஆம்
  - item: i6, பச்சை, cube மற்றும் no
  - item: i7, நீலம், cube மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na kijani
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict ndiyo kama: theory1, umbo na tufe
  - predict ndiyo kama: theory2, umbo na tufe
  - predict ndiyo kama: theory3, rangi na kijani
  - predict ndiyo kama: theory3, rangi na ekundu
  - predict ndiyo kama: theory0, umbo na tufe
Cost model: exception gharimu: 4.
Data:
  - item: i4, buluu, tufe na hapana
  - item: i0, buluu, tufe na hapana
  - item: i3, buluu, tufe na hapana
  - item: i5, kijani, mchemraba na hapana
  - item: i1, ekundu, tufe na hapana
  - item: i2, kijani, tufe na ndiyo
  - item: i6, kijani, mchemraba na hapana
  - item: i7, buluu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory0, shape dóó sphere
Cost model: exception bą́ą́h ílį́ 4.
Data:
  - item: i4, dootłʼizh, sphere dóó no
  - item: i0, dootłʼizh, sphere dóó no
  - item: i3, dootłʼizh, sphere dóó no
  - item: i5, dinootłʼizh, cube dóó no
  - item: i1, łichííʼ, sphere dóó no
  - item: i2, dinootłʼizh, sphere dóó aooʼ
  - item: i6, dinootłʼizh, cube dóó no
  - item: i7, dootłʼizh, cube dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - predict aap if: theory2, qalipaat aamma qorsuk
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict aap if: theory1, shape aamma sphere
  - predict aap if: theory2, shape aamma sphere
  - predict aap if: theory3, qalipaat aamma qorsuk
  - predict aap if: theory3, qalipaat aamma aappalajaarpoq
  - predict aap if: theory0, shape aamma sphere
Cost model: exception cost 4.
Data:
  - item: i4, tungujortoq, sphere aamma no
  - item: i0, tungujortoq, sphere aamma no
  - item: i3, tungujortoq, sphere aamma no
  - item: i5, qorsuk, cube aamma no
  - item: i1, aappalajaarpoq, sphere aamma no
  - item: i2, qorsuk, sphere aamma aap
  - item: i6, qorsuk, cube aamma no
  - item: i7, tungujortoq, cube aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ya sapos: theory2, kala na grinpela
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict ya sapos: theory1, shape na sphere
  - predict ya sapos: theory2, shape na sphere
  - predict ya sapos: theory3, kala na grinpela
  - predict ya sapos: theory3, kala na ret
  - predict ya sapos: theory0, shape na sphere
Cost model: exception cost 4.
Data:
  - item: i4, blu, sphere na no
  - item: i0, blu, sphere na no
  - item: i3, blu, sphere na no
  - item: i5, grinpela, cube na no
  - item: i1, ret, sphere na no
  - item: i2, grinpela, sphere na ya
  - item: i6, grinpela, cube na no
  - item: i7, blu, cube na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, color green
  - predict yes if: theory3, color màxke
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, màxke, sphere no
  - item: i2, green, sphere yes
  - item: i6, green, cube no
  - item: i7, blue, cube yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - predict yes if: theory2, бос а баьццара
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape а sphere
  - predict yes if: theory2, shape а sphere
  - predict yes if: theory3, бос а баьццара
  - predict yes if: theory3, бос а цӏе
  - predict yes if: theory0, shape а sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere а no
  - item: i0, blue, sphere а no
  - item: i3, blue, sphere а no
  - item: i5, баьццара, cube а no
  - item: i1, цӏе, sphere а no
  - item: i2, баьццара, sphere а yes
  - item: i6, баьццара, cube а no
  - item: i7, blue, cube а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - 예언하다 네 만약: theory2, 색깔 녹색의
  - theory0 2 이론 크기
  - theory1 2 이론 크기
  - theory3 4 이론 크기
  - theory2 4 이론 크기
  - 예언하다 네 만약: theory1, 모양 구체
  - 예언하다 네 만약: theory2, 모양 구체
  - 예언하다 네 만약: theory3, 색깔 녹색의
  - 예언하다 네 만약: theory3, 색깔 빨갛다
  - 예언하다 네 만약: theory0, 모양 구체
cost model: 예외 cost 4.
자료:
  - 아이템: i4, 푸르다, 구체 아니요
  - 아이템: i0, 푸르다, 구체 아니요
  - 아이템: i3, 푸르다, 구체 아니요
  - 아이템: i5, 녹색의, 정육면체 아니요
  - 아이템: i1, 빨갛다, 구체 아니요
  - 아이템: i2, 녹색의, 구체 네
  - 아이템: i6, 녹색의, 정육면체 아니요
  - 아이템: i7, 푸르다, 정육면체 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - aurresan bai if: theory2, kolore eta berde
  - theory0 2 theory tamaina
  - theory1 2 theory tamaina
  - theory3 4 theory tamaina
  - theory2 4 theory tamaina
  - aurresan bai if: theory1, shape eta esfera
  - aurresan bai if: theory2, shape eta esfera
  - aurresan bai if: theory3, kolore eta berde
  - aurresan bai if: theory3, kolore eta gorri
  - aurresan bai if: theory0, shape eta esfera
Cost model: exception kostatu 4.
Data:
  - item: i4, urdin, esfera eta no
  - item: i0, urdin, esfera eta no
  - item: i3, urdin, esfera eta no
  - item: i5, berde, kubo eta no
  - item: i1, gorri, esfera eta no
  - item: i2, berde, esfera eta bai
  - item: i6, berde, kubo eta no
  - item: i7, urdin, kubo eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict ᎥᎥ if: theory1, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory2, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory0, shape ᎠᎴ sphere
Cost model: exception cost 4.
Data:
  - item: i4, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i0, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i3, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i1, ᎩᎦᎨ, sphere ᎠᎴ ᏝᏃ
  - item: i2, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᎥᎥ
  - item: i6, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i7, ᏌᎪᏂᎨᎢ, cube ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict arí if: theory2, llimphi waylla
  - theory0 theory chhika 2
  - theory1 theory chhika 2
  - theory3 theory chhika 4
  - theory2 theory chhika 4
  - predict arí if: theory1, shape sphere
  - predict arí if: theory2, shape sphere
  - predict arí if: theory3, llimphi waylla
  - predict arí if: theory3, llimphi puka
  - predict arí if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, anqas, sphere mánan
  - item: i0, anqas, sphere mánan
  - item: i3, anqas, sphere mánan
  - item: i5, waylla, cube mánan
  - item: i1, puka, sphere mánan
  - item: i2, waylla, sphere arí
  - item: i6, waylla, cube mánan
  - item: i7, anqas, cube arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color ыкӏи уцышъо
  - theory0 2 тиориэ size
  - theory1 2 тиориэ size
  - theory3 4 тиориэ size
  - theory2 4 тиориэ size
  - predict yes if: theory1, shape ыкӏи sphere
  - predict yes if: theory2, shape ыкӏи sphere
  - predict yes if: theory3, color ыкӏи уцышъо
  - predict yes if: theory3, color ыкӏи плъыжьы
  - predict yes if: theory0, shape ыкӏи sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere ыкӏи no
  - item: i0, blue, sphere ыкӏи no
  - item: i3, blue, sphere ыкӏи no
  - item: i5, уцышъо, cube ыкӏи no
  - item: i1, плъыжьы, sphere ыкӏи no
  - item: i2, уцышъо, sphere ыкӏи yes
  - item: i6, уцышъо, cube ыкӏи no
  - item: i7, blue, cube ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - predict yes if: theory2, xim thiab ntsuab
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape thiab sphere
  - predict yes if: theory2, shape thiab sphere
  - predict yes if: theory3, xim thiab ntsuab
  - predict yes if: theory3, xim thiab liab
  - predict yes if: theory0, shape thiab sphere
Cost model: exception nqi 4.
Data:
  - item: i4, xiav, sphere thiab no
  - item: i0, xiav, sphere thiab no
  - item: i3, xiav, sphere thiab no
  - item: i5, ntsuab, cube thiab no
  - item: i1, liab, sphere thiab no
  - item: i2, ntsuab, sphere thiab yes
  - item: i6, ntsuab, cube thiab no
  - item: i7, xiav, cube thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - predict ngai if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict ngai if: theory1, shape sphere
  - predict ngai if: theory2, shape sphere
  - predict ngai if: theory3, color green
  - predict ngai if: theory3, color red
  - predict ngai if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere woi
  - item: i0, blue, sphere woi
  - item: i3, blue, sphere woi
  - item: i5, green, cube woi
  - item: i1, red, sphere woi
  - item: i2, green, sphere ngai
  - item: i6, green, cube woi
  - item: i7, blue, cube ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - predict i̠i if: theory2, color micha' green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict i̠i if: theory1, shape micha' sphere
  - predict i̠i if: theory2, shape micha' sphere
  - predict i̠i if: theory3, color micha' green
  - predict i̠i if: theory3, color micha' homa
  - predict i̠i if: theory0, shape micha' sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere micha' no
  - item: i0, blue, sphere micha' no
  - item: i3, blue, sphere micha' no
  - item: i5, green, cube micha' no
  - item: i1, homa, sphere micha' no
  - item: i2, green, sphere micha' i̠i
  - item: i6, green, cube micha' no
  - item: i7, blue, cube micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - predict yes if: theory2, color カ green
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict yes if: theory1, shape カ sphere
  - predict yes if: theory2, shape カ sphere
  - predict yes if: theory3, color カ green
  - predict yes if: theory3, color カ フレ
  - predict yes if: theory0, shape カ sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere カ ソモ
  - item: i0, blue, sphere カ ソモ
  - item: i3, blue, sphere カ ソモ
  - item: i5, green, cube カ ソモ
  - item: i1, フレ, sphere カ ソモ
  - item: i2, green, sphere カ yes
  - item: i6, green, cube カ ソモ
  - item: i7, blue, cube カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes эвыр: theory2, пэран ынкъам green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes эвыр: theory1, shape ынкъам sphere
  - predict yes эвыр: theory2, shape ынкъам sphere
  - predict yes эвыр: theory3, пэран ынкъам green
  - predict yes эвыр: theory3, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory0, shape ынкъам sphere
Cost model: exception cost 4.
Data:
  - item: i4, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i0, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i3, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i5, green, cube ынкъам no
  - item: i1, чеԓгыԓьын, sphere ынкъам no
  - item: i2, green, sphere ынкъам yes
  - item: i6, green, cube ынкъам no
  - item: i7, ныԓиԓиԓӄин, cube ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes wa: theory2, color yéetel yaʼax
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes wa: theory1, shape yéetel sphere
  - predict yes wa: theory2, shape yéetel sphere
  - predict yes wa: theory3, color yéetel yaʼax
  - predict yes wa: theory3, color yéetel chak
  - predict yes wa: theory0, shape yéetel sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere yéetel no
  - item: i0, blue, sphere yéetel no
  - item: i3, blue, sphere yéetel no
  - item: i5, yaʼax, cube yéetel no
  - item: i1, chak, sphere yéetel no
  - item: i2, yaʼax, sphere yéetel yes
  - item: i6, yaʼax, cube yéetel no
  - item: i7, blue, cube yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict hą if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict hą if: theory1, shape sphere
  - predict hą if: theory2, shape sphere
  - predict hą if: theory3, color green
  - predict hą if: theory3, color red
  - predict hą if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, red, sphere no
  - item: i2, green, sphere hą
  - item: i6, green, cube no
  - item: i7, blue, cube hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - predict yes if: theory2, color na green
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict yes if: theory1, shape na sphere
  - predict yes if: theory2, shape na sphere
  - predict yes if: theory3, color na green
  - predict yes if: theory3, color na red
  - predict yes if: theory0, shape na sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere na no
  - item: i0, blue, sphere na no
  - item: i3, blue, sphere na no
  - item: i5, green, cube na no
  - item: i1, red, sphere na no
  - item: i2, green, sphere na yes
  - item: i6, green, cube na no
  - item: i7, blue, cube na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict heẽ if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict heẽ if: theory1, shape sphere
  - predict heẽ if: theory2, shape sphere
  - predict heẽ if: theory3, color green
  - predict heẽ if: theory3, color pytã
  - predict heẽ if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere nahániri
  - item: i0, blue, sphere nahániri
  - item: i3, blue, sphere nahániri
  - item: i5, green, cube nahániri
  - item: i1, pytã, sphere nahániri
  - item: i2, green, sphere heẽ
  - item: i6, green, cube nahániri
  - item: i7, blue, cube heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict jisa if: theory2, sami ch’uxña
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict jisa if: theory1, shape sphere
  - predict jisa if: theory2, shape sphere
  - predict jisa if: theory3, sami ch’uxña
  - predict jisa if: theory3, sami chupika
  - predict jisa if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, larama, sphere no
  - item: i0, larama, sphere no
  - item: i3, larama, sphere no
  - item: i5, ch’uxña, cube no
  - item: i1, chupika, sphere no
  - item: i2, ch’uxña, sphere jisa
  - item: i6, ch’uxña, cube no
  - item: i7, larama, cube jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ʻée if: theory2, color ǂʻá green
  - theory0 theory ʻǁnàhã 2
  - theory1 theory ʻǁnàhã 2
  - theory3 theory ʻǁnàhã 4
  - theory2 theory ʻǁnàhã 4
  - predict ʻée if: theory1, shape ǂʻá sphere
  - predict ʻée if: theory2, shape ǂʻá sphere
  - predict ʻée if: theory3, color ǂʻá green
  - predict ʻée if: theory3, color ǂʻá red
  - predict ʻée if: theory0, shape ǂʻá sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere ǂʻá ʻĩĩ
  - item: i0, blue, sphere ǂʻá ʻĩĩ
  - item: i3, blue, sphere ǂʻá ʻĩĩ
  - item: i5, green, cube ǂʻá ʻĩĩ
  - item: i1, red, sphere ǂʻá ʻĩĩ
  - item: i2, green, sphere ǂʻá ʻée
  - item: i6, green, cube ǂʻá ʻĩĩ
  - item: i7, blue, cube ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, вал green
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, вал green
  - predict yes if: theory3, вал red
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, red, sphere no
  - item: i2, green, sphere yes
  - item: i6, green, cube no
  - item: i7, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, color green
  - predict yes if: theory3, color red
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, red, sphere no
  - item: i2, green, sphere yes
  - item: i6, green, cube no
  - item: i7, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, color green
  - predict yes if: theory3, color red
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, red, sphere no
  - item: i2, green, sphere yes
  - item: i6, green, cube no
  - item: i7, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ee if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict ee if: theory1, shape sphere
  - predict ee if: theory2, shape sphere
  - predict ee if: theory3, color green
  - predict ee if: theory3, color red
  - predict ee if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, red, sphere no
  - item: i2, green, sphere ee
  - item: i6, green, cube no
  - item: i7, blue, cube ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, color green
  - predict yes if: theory3, color red
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere inte
  - item: i0, blue, sphere inte
  - item: i3, blue, sphere inte
  - item: i5, green, cube inte
  - item: i1, red, sphere inte
  - item: i2, green, sphere yes
  - item: i6, green, cube inte
  - item: i7, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - prédire oui si: theory2, couleur et vert
  - theory0 théorie taille 2
  - theory1 théorie taille 2
  - theory3 théorie taille 4
  - theory2 théorie taille 4
  - prédire oui si: theory1, forme et sphère
  - prédire oui si: theory2, forme et sphère
  - prédire oui si: theory3, couleur et vert
  - prédire oui si: theory3, couleur et rouge
  - prédire oui si: theory0, forme et sphère
Coûts : exception coûter 4.
Les données sont:
  - article: i4, bleu, sphère et no
  - article: i0, bleu, sphère et no
  - article: i3, bleu, sphère et no
  - article: i5, vert, cube et no
  - article: i1, rouge, sphère et no
  - article: i2, vert, sphère et oui
  - article: i6, vert, cube et no
  - article: i7, bleu, cube et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - predict jes se: theory2, koloro kaj verda
  - theory0 teorio grandeco 2
  - theory1 teorio grandeco 2
  - theory3 teorio grandeco 4
  - theory2 teorio grandeco 4
  - predict jes se: theory1, formi kaj sfero
  - predict jes se: theory2, formi kaj sfero
  - predict jes se: theory3, koloro kaj verda
  - predict jes se: theory3, koloro kaj ruĝa
  - predict jes se: theory0, formi kaj sfero
Kosto pozistino: escepto kosti 4.
Datumo:
  - ero: i4, blua, sfero kaj no
  - ero: i0, blua, sfero kaj no
  - ero: i3, blua, sfero kaj no
  - ero: i5, verda, kubo kaj no
  - ero: i1, ruĝa, sfero kaj no
  - ero: i2, verda, sfero kaj jes
  - ero: i6, verda, kubo kaj no
  - ero: i7, blua, kubo kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - megjósol igen ha: theory2, szín és zöld
  - theory0 elmélet méret 2
  - theory1 elmélet méret 2
  - theory3 elmélet méret 4
  - theory2 elmélet méret 4
  - megjósol igen ha: theory1, alak és gömb
  - megjósol igen ha: theory2, alak és gömb
  - megjósol igen ha: theory3, szín és zöld
  - megjósol igen ha: theory3, szín és piros
  - megjósol igen ha: theory0, alak és gömb
Költség manöken: kivétel kerül 4.
Adat:
  - cikk: i4, kék, gömb és no
  - cikk: i0, kék, gömb és no
  - cikk: i3, kék, gömb és no
  - cikk: i5, zöld, kocka és no
  - cikk: i1, piros, gömb és no
  - cikk: i2, zöld, gömb és igen
  - cikk: i6, zöld, kocka és no
  - cikk: i7, kék, kocka és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - predict係要是:theory2, 色及綠色嘅
  - theory0theory size2
  - theory1theory size2
  - theory3theory size4
  - theory2theory size4
  - predict係要是:theory1, 形狀及sphere
  - predict係要是:theory2, 形狀及sphere
  - predict係要是:theory3, 色及綠色嘅
  - predict係要是:theory3, 色及紅
  - predict係要是:theory0, 形狀及sphere
cost model:exception cost:4.
數據:
  - item:i4, 藍色嘅, sphere及no
  - item:i0, 藍色嘅, sphere及no
  - item:i3, 藍色嘅, sphere及no
  - item:i5, 綠色嘅, cube及no
  - item:i1, 紅, sphere及no
  - item:i2, 綠色嘅, sphere及係
  - item:i6, 綠色嘅, cube及no
  - item:i7, 藍色嘅, cube及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - predict yes if: theory2, color とぅ green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape とぅ sphere
  - predict yes if: theory2, shape とぅ sphere
  - predict yes if: theory3, color とぅ green
  - predict yes if: theory3, color とぅ red
  - predict yes if: theory0, shape とぅ sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere とぅ no
  - item: i0, blue, sphere とぅ no
  - item: i3, blue, sphere とぅ no
  - item: i5, green, cube とぅ no
  - item: i1, red, sphere とぅ no
  - item: i2, green, sphere とぅ yes
  - item: i6, green, cube とぅ no
  - item: i7, blue, cube とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - predict иә егер: theory2, түс және жасыл
  - theory0 теория мөлшер 2
  - theory1 теория мөлшер 2
  - theory3 теория мөлшер 4
  - theory2 теория мөлшер 4
  - predict иә егер: theory1, нысан және сфера
  - predict иә егер: theory2, нысан және сфера
  - predict иә егер: theory3, түс және жасыл
  - predict иә егер: theory3, түс және қызыл
  - predict иә егер: theory0, нысан және сфера
Тұру үлгі: ерекшелік тұру 4.
Деректер:
  - зат: i4, көк, сфера және жоқ
  - зат: i0, көк, сфера және жоқ
  - зат: i3, көк, сфера және жоқ
  - зат: i5, жасыл, куб және жоқ
  - зат: i1, қызыл, сфера және жоқ
  - зат: i2, жасыл, сфера және иә
  - зат: i6, жасыл, куб және жоқ
  - зат: i7, көк, куб және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - predict כֵּן אִם: theory2, צֶבַע וְ־ ירוק מקנאה
  - theory0 תּוֹרָה גֹּדֶל 2
  - theory1 תּוֹרָה גֹּדֶל 2
  - theory3 תּוֹרָה גֹּדֶל 4
  - theory2 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory1, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory2, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory3, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory3, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory0, צוּרָה וְ־ כַּדּוּר
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 4.
נְתוּנִים:
  - אבזר: i4, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i0, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i3, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i5, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i1, אָדוֹם, כַּדּוּר וְ־ no
  - אבזר: i2, ירוק מקנאה, כַּדּוּר וְ־ כֵּן
  - אבזר: i6, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i7, כָּחוֹל, קובייה וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - predict იახ if: theory2, color დო green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict იახ if: theory1, shape დო sphere
  - predict იახ if: theory2, shape დო sphere
  - predict იახ if: theory3, color დო green
  - predict იახ if: theory3, color დო red
  - predict იახ if: theory0, shape დო sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere დო no
  - item: i0, blue, sphere დო no
  - item: i3, blue, sphere დო no
  - item: i5, green, cube დო no
  - item: i1, red, sphere დო no
  - item: i2, green, sphere დო იახ
  - item: i6, green, cube დო no
  - item: i7, blue, cube დო იახ
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - predict ya jika: theory2, warna dan hijau
  - theory0 teori ukuran 2
  - theory1 teori ukuran 2
  - theory3 teori ukuran 4
  - theory2 teori ukuran 4
  - predict ya jika: theory1, bentuk dan bola
  - predict ya jika: theory2, bentuk dan bola
  - predict ya jika: theory3, warna dan hijau
  - predict ya jika: theory3, warna dan merah
  - predict ya jika: theory0, bentuk dan bola
Cost model: pengecualian menghabiskan biaya 4.
Data:
  - benda: i4, biru, bola dan no
  - benda: i0, biru, bola dan no
  - benda: i3, biru, bola dan no
  - benda: i5, hijau, kubus dan no
  - benda: i1, merah, bola dan no
  - benda: i2, hijau, bola dan ya
  - benda: i6, hijau, kubus dan no
  - benda: i7, biru, kubus dan ya
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងបៃតង
  - theory0ទ្រឹស្ដីទំហំ2
  - theory1ទ្រឹស្ដីទំហំ2
  - theory3ទ្រឹស្ដីទំហំ4
  - theory2ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory2, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory0, គ្រោងនឹងគោល
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:4.
ទិន្នន័យ:
  - item:i4, ខៀវ, គោលនឹងទេ
  - item:i0, ខៀវ, គោលនឹងទេ
  - item:i3, ខៀវ, គោលនឹងទេ
  - item:i5, បៃតង, គូបនឹងទេ
  - item:i1, ក្រហម, គោលនឹងទេ
  - item:i2, បៃតង, គោលនឹងច៎ា
  - item:i6, បៃតង, គូបនឹងទេ
  - item:i7, ខៀវ, គូបនឹងច៎ា
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຂຽວ
  - theory0ທິດສະດີຂະຫນາດ2
  - theory1ທິດສະດີຂະຫນາດ2
  - theory3ທິດສະດີຂະຫນາດ4
  - theory2ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ຮູບຮ່າງແລະຄະດີ
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:4.
ຂໍ້ມູນ:
  - item:i4, ຟ້າ, ຄະດີແລະno
  - item:i0, ຟ້າ, ຄະດີແລະno
  - item:i3, ຟ້າ, ຄະດີແລະno
  - item:i5, ຂຽວ, ລູກບາດແລະno
  - item:i1, ແດງ, ຄະດີແລະno
  - item:i2, ຂຽວ, ຄະດີແລະແມ່ນ
  - item:i6, ຂຽວ, ລູກບາດແລະno
  - item:i7, ຟ້າ, ລູກບາດແລະແມ່ນ
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - predict అవును అయితే: theory2, రంగు మరియు పచ్చని
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict అవును అయితే: theory1, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory2, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory3, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory3, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory0, ఆకారం మరియు గోళము
cost model: exception cost 4.
data:
  - item: i4, నీలము, గోళము మరియు no
  - item: i0, నీలము, గోళము మరియు no
  - item: i3, నీలము, గోళము మరియు no
  - item: i5, పచ్చని, cube మరియు no
  - item: i1, ఎర్ర, గోళము మరియు no
  - item: i2, పచ్చని, గోళము మరియు అవును
  - item: i6, పచ్చని, cube మరియు no
  - item: i7, నీలము, cube మరియు అవును
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - predict hẹn if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict hẹn if: theory1, shape sphere
  - predict hẹn if: theory2, shape sphere
  - predict hẹn if: theory3, color green
  - predict hẹn if: theory3, color pupa
  - predict hẹn if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, dúdú, sphere rárá
  - item: i0, dúdú, sphere rárá
  - item: i3, dúdú, sphere rárá
  - item: i5, green, cube rárá
  - item: i1, pupa, sphere rárá
  - item: i2, green, sphere bẹ́ẹ̀ ni
  - item: i6, green, cube rárá
  - item: i7, dúdú, cube bẹ́ẹ̀ ni
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict au if: theory2, color green
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict au if: theory1, shape sphere
  - predict au if: theory2, shape sphere
  - predict au if: theory3, color green
  - predict au if: theory3, color łichíí
  - predict au if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, łichíí, sphere no
  - item: i2, green, sphere au
  - item: i6, green, cube no
  - item: i7, blue, cube au
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - predict yes if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, color green
  - predict yes if: theory3, color red
  - predict yes if: theory0, shape sphere
cost model: exception cost 4.
data:
  - item: i4, blue, sphere ᐋᒃᑲ
  - item: i0, blue, sphere ᐋᒃᑲ
  - item: i3, blue, sphere ᐋᒃᑲ
  - item: i5, green, cube ᐋᒃᑲ
  - item: i1, red, sphere ᐋᒃᑲ
  - item: i2, green, sphere yes
  - item: i6, green, cube ᐋᒃᑲ
  - item: i7, blue, cube yes
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color grun
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, color grun
  - predict yes if: theory3, color red
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blaw, sphere no
  - item: i0, blaw, sphere no
  - item: i3, blaw, sphere no
  - item: i5, grun, cube no
  - item: i1, red, sphere no
  - item: i2, grun, sphere yes
  - item: i6, grun, cube no
  - item: i7, blaw, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory0 theory size 2
  - theory1 theory size 2
  - theory3 theory size 4
  - theory2 theory size 4
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, color green
  - predict yes if: theory3, color red
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, green, cube no
  - item: i1, red, sphere no
  - item: i2, green, sphere yes
  - item: i6, green, cube no
  - item: i7, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, ранг къацу
  - theory0 2 theory size
  - theory1 2 theory size
  - theory3 4 theory size
  - theory2 4 theory size
  - predict yes if: theory1, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory3, ранг къацу
  - predict yes if: theory3, ранг red
  - predict yes if: theory0, shape sphere
Cost model: exception cost 4.
Data:
  - item: i4, blue, sphere no
  - item: i0, blue, sphere no
  - item: i3, blue, sphere no
  - item: i5, къацу, cube no
  - item: i1, red, sphere no
  - item: i2, къацу, sphere yes
  - item: i6, къацу, cube no
  - item: i7, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - theory2 theory size 2
  - predicts yes if: theory0, colour and green
  - predicts yes if: theory0, shape and cone
  - predicts yes if: theory1, shape and sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, colour and red
  - predicts yes if: theory3, shape and cone
  - theory0 theory size 4
Costs: exception cost 4.
The data are:
  - item: i5, green, cone and no
  - item: i0, green, cone and yes
  - item: i7, green, sphere and yes
  - item: i1, green, sphere and no
  - item: i6, red, cube and yes
  - item: i2, blue, cone and yes
  - item: i3, red, cube and no
  - item: i4, green, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - theory2 el tamaño de la teoría 2
  - predice sí si: theory0, es de color y verde
  - predice sí si: theory0, forma y cono
  - predice sí si: theory1, forma y esfera
  - theory1 el tamaño de la teoría 2
  - theory3 el tamaño de la teoría 4
  - predice sí si: theory3, forma y esfera
  - predice sí si: theory2, es de color y rojo
  - predice sí si: theory3, forma y cono
  - theory0 el tamaño de la teoría 4
Costes: exception el coste: 4.
Los datos son:
  - el elemento: i5, verde, cono y no
  - el elemento: i0, verde, cono y sí
  - el elemento: i7, verde, esfera y sí
  - el elemento: i1, verde, esfera y no
  - el elemento: i6, rojo, cubo y sí
  - el elemento: i2, azul, cono y sí
  - el elemento: i3, rojo, cubo y no
  - el elemento: i4, verde, cubo y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - theory2的理论规模是2
  - 预测是若：theory0、颜色和绿色的
  - 预测是若：theory0、形状和圆锥
  - 预测是若：theory1、形状和球体
  - theory1的理论规模是2
  - theory3的理论规模是4
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、颜色和红色的
  - 预测是若：theory3、形状和圆锥
  - theory0的理论规模是4
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i5、绿色的、圆锥和否
  - 的物品是：i0、绿色的、圆锥和是
  - 的物品是：i7、绿色的、球体和是
  - 的物品是：i1、绿色的、球体和否
  - 的物品是：i6、红色的、立方体和是
  - 的物品是：i2、蓝色的、圆锥和是
  - 的物品是：i3、红色的、立方体和否
  - 的物品是：i4、绿色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - theory2 2 kuram boyut
  - öngörür evet eğer: theory0, renk ve yeşil
  - öngörür evet eğer: theory0, şekil ve koni
  - öngörür evet eğer: theory1, şekil ve küre
  - theory1 2 kuram boyut
  - theory3 4 kuram boyut
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, renk ve kırmızı
  - öngörür evet eğer: theory3, şekil ve koni
  - theory0 4 kuram boyut
Cost model: exception maliyet: 4.
Veriler:
  - item: i5, yeşil, koni ve hayır
  - item: i0, yeşil, koni ve evet
  - item: i7, yeşil, küre ve evet
  - item: i1, yeşil, küre ve hayır
  - item: i6, kırmızı, küp ve evet
  - item: i2, mavi, koni ve evet
  - item: i3, kırmızı, küp ve hayır
  - item: i4, yeşil, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - theory2 theory size 2
  - predict ndiyo kama: theory0, rangi na kijani
  - predict ndiyo kama: theory0, umbo na koni
  - predict ndiyo kama: theory1, umbo na tufe
  - theory1 theory size 2
  - theory3 theory size 4
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, rangi na ekundu
  - predict ndiyo kama: theory3, umbo na koni
  - theory0 theory size 4
Cost model: exception gharimu: 4.
Data:
  - item: i5, kijani, koni na hapana
  - item: i0, kijani, koni na ndiyo
  - item: i7, kijani, tufe na ndiyo
  - item: i1, kijani, tufe na hapana
  - item: i6, ekundu, mchemraba na ndiyo
  - item: i2, buluu, koni na ndiyo
  - item: i3, ekundu, mchemraba na hapana
  - item: i4, kijani, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - theory2 theory size 2
  - predicts yes if: theory0, colour and green
  - predicts yes if: theory0, shape and cone
  - predicts yes if: theory1, shape and sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, colour and red
  - predicts yes if: theory3, shape and cone
  - theory0 theory size 4
Costs: exception cost 4.
The data are:
  - item: i5, green, cone and no
  - item: i0, green, cone and yes
  - item: i7, green, sphere and yes
  - item: i1, green, sphere and no
  - item: i6, red, cube and yes
  - item: i2, blue, cone and yes
  - item: i3, red, cube and no
  - item: i4, green, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(theory_size theory2 2) (predicts_yes_if theory0 color green) (predicts_yes_if theory0 shape cone) (predicts_yes_if theory1 shape sphere) (theory_size theory1 2) (theory_size theory3 4) (predicts_yes_if theory3 shape sphere) (predicts_yes_if theory2 color red) (predicts_yes_if theory3 shape cone) (theory_size theory0 4)], cost_model: [(exception_cost 4)], data: [(item i5 green cone no) (item i0 green cone yes) (item i7 green sphere yes) (item i1 green sphere no) (item i6 red cube yes) (item i2 blue cone yes) (item i3 red cube no) (item i4 green cube yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - theory2 Theorie Größe 2
  - vorhersagen ja wenn: theory0, Farbe und grün
  - vorhersagen ja wenn: theory0, Form und Kegel
  - vorhersagen ja wenn: theory1, Form und Kugel
  - theory1 Theorie Größe 2
  - theory3 Theorie Größe 4
  - vorhersagen ja wenn: theory3, Form und Kugel
  - vorhersagen ja wenn: theory2, Farbe und rot
  - vorhersagen ja wenn: theory3, Form und Kegel
  - theory0 Theorie Größe 4
Kosten: Ausnahme kosten 4.
Die Daten sind:
  - Ding: i5, grün, Kegel und nein
  - Ding: i0, grün, Kegel und ja
  - Ding: i7, grün, Kugel und ja
  - Ding: i1, grün, Kugel und nein
  - Ding: i6, rot, Kubus und ja
  - Ding: i2, blau, Kegel und ja
  - Ding: i3, rot, Kubus und nein
  - Ding: i4, grün, Kubus und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - theory2 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory0, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory0, о̏блӣк и stožac
  - predvidjeti да ȁko: theory1, о̏блӣк и сфе́ра
  - theory1 тѐо̄рија величѝна 2
  - theory3 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory3, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory2, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory3, о̏блӣк и stožac
  - theory0 тѐо̄рија величѝна 4
Trošak мо̀дел: изузе́так ко̏штати 4.
Податци:
  - item: i5, зѐлен, stožac и no
  - item: i0, зѐлен, stožac и да
  - item: i7, зѐлен, сфе́ра и да
  - item: i1, зѐлен, сфе́ра и no
  - item: i6, цр̀вен, коцка и да
  - item: i2, пла̑в, stožac и да
  - item: i3, цр̀вен, коцка и no
  - item: i4, зѐлен, коцка и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - theory2 teoria koko 2
  - ennakoida kyllä jos: theory0, väri ja vihreä
  - ennakoida kyllä jos: theory0, muoto ja kartio
  - ennakoida kyllä jos: theory1, muoto ja pallo
  - theory1 teoria koko 2
  - theory3 teoria koko 4
  - ennakoida kyllä jos: theory3, muoto ja pallo
  - ennakoida kyllä jos: theory2, väri ja punainen
  - ennakoida kyllä jos: theory3, muoto ja kartio
  - theory0 teoria koko 4
Kustannus malli: poikkeus maksaa 4.
Tieto:
  - esine: i5, vihreä, kartio ja ei
  - esine: i0, vihreä, kartio ja kyllä
  - esine: i7, vihreä, pallo ja kyllä
  - esine: i1, vihreä, pallo ja ei
  - esine: i6, punainen, kuutio ja kyllä
  - esine: i2, sininen, kartio ja kyllä
  - esine: i3, punainen, kuutio ja ei
  - esine: i4, vihreä, kuutio ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - theory2的理论规模是2
  - 预测是若：theory0、颜色和绿色的
  - 预测是若：theory0、形状和圆锥
  - 预测是若：theory1、形状和球体
  - theory1的理论规模是2
  - theory3的理论规模是4
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、颜色和红色的
  - 预测是若：theory3、形状和圆锥
  - theory0的理论规模是4
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i5、绿色的、圆锥和否
  - 的物品是：i0、绿色的、圆锥和是
  - 的物品是：i7、绿色的、球体和是
  - 的物品是：i1、绿色的、球体和否
  - 的物品是：i6、红色的、立方体和是
  - 的物品是：i2、蓝色的、圆锥和是
  - 的物品是：i3、红色的、立方体和否
  - 的物品是：i4、绿色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - theory22理論大きさ
  - 予言するはいもし...:theory0, 色と緑の
  - 予言するはいもし...:theory0, 形と松毬
  - 予言するはいもし...:theory1, 形と球
  - theory12理論大きさ
  - theory34理論大きさ
  - 予言するはいもし...:theory3, 形と球
  - 予言するはいもし...:theory2, 色と赤い
  - 予言するはいもし...:theory3, 形と松毬
  - theory04理論大きさ
費用モデル:例外費やす:4.
資料:
  - 項目:i5, 緑の, 松毬といいえ
  - 項目:i0, 緑の, 松毬とはい
  - 項目:i7, 緑の, 球とはい
  - 項目:i1, 緑の, 球といいえ
  - 項目:i6, 赤い, 立方体とはい
  - 項目:i2, 青い, 松毬とはい
  - 項目:i3, 赤い, 立方体といいえ
  - 項目:i4, 緑の, 立方体とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - theory2 2 kuram boyut
  - öngörür evet eğer: theory0, renk ve yeşil
  - öngörür evet eğer: theory0, şekil ve koni
  - öngörür evet eğer: theory1, şekil ve küre
  - theory1 2 kuram boyut
  - theory3 4 kuram boyut
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, renk ve kırmızı
  - öngörür evet eğer: theory3, şekil ve koni
  - theory0 4 kuram boyut
Cost model: exception maliyet: 4.
Veriler:
  - item: i5, yeşil, koni ve hayır
  - item: i0, yeşil, koni ve evet
  - item: i7, yeşil, küre ve evet
  - item: i1, yeşil, küre ve hayır
  - item: i6, kırmızı, küp ve evet
  - item: i2, mavi, koni ve evet
  - item: i3, kırmızı, küp ve hayır
  - item: i4, yeşil, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - نَظَرِيَّة مَقَاس theory2 2
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory0، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ كُرَة
  - نَظَرِيَّة مَقَاس theory1 2
  - نَظَرِيَّة مَقَاس theory3 4
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ مَخْرُوط
  - نَظَرِيَّة مَقَاس theory0 4
cost model: اِسْتِثْنَاء cost 4.
بَيَانَات:
  - بَنْد: i5، أَخْضَر، مَخْرُوط وَـ no
  - بَنْد: i0، أَخْضَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i7، أَخْضَر، كُرَة وَـ نَعَم
  - بَنْد: i1، أَخْضَر، كُرَة وَـ no
  - بَنْد: i6، أَحْمَر، كَعْبَة وَـ نَعَم
  - بَنْد: i2، أَزْرَق، مَخْرُوط وَـ نَعَم
  - بَنْد: i3، أَحْمَر، كَعْبَة وَـ no
  - بَنْد: i4، أَخْضَر، كَعْبَة وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - theory2 2 თეორია ზომა
  - predict დიახ თუ: theory0, ფერი და მწვანე
  - predict დიახ თუ: theory0, ფორმა და გირჩა
  - predict დიახ თუ: theory1, ფორმა და სფერო
  - theory1 2 თეორია ზომა
  - theory3 4 თეორია ზომა
  - predict დიახ თუ: theory3, ფორმა და სფერო
  - predict დიახ თუ: theory2, ფერი და წითელი
  - predict დიახ თუ: theory3, ფორმა და გირჩა
  - theory0 4 თეორია ზომა
Ფასი მოდელი: გამონაკლისი ღირს 4.
Მონაცემები:
  - item: i5, მწვანე, გირჩა და no
  - item: i0, მწვანე, გირჩა და დიახ
  - item: i7, მწვანე, სფერო და დიახ
  - item: i1, მწვანე, სფერო და no
  - item: i6, წითელი, კუბი და დიახ
  - item: i2, ლურჯი, გირჩა და დიახ
  - item: i3, წითელი, კუბი და no
  - item: i4, მწვანე, კუბი და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - ariā size theory2 2
  - matakite āe mēnā: theory0, kano me kākāriki
  - matakite āe mēnā: theory0, āhuahanga me koeko
  - matakite āe mēnā: theory1, āhuahanga me poi
  - ariā size theory1 2
  - ariā size theory3 4
  - matakite āe mēnā: theory3, āhuahanga me poi
  - matakite āe mēnā: theory2, kano me red
  - matakite āe mēnā: theory3, āhuahanga me koeko
  - ariā size theory0 4
Cost model: hāungatanga cost 4.
Hōtuku:
  - tūemi: i5, kākāriki, koeko me kāo
  - tūemi: i0, kākāriki, koeko me āe
  - tūemi: i7, kākāriki, poi me āe
  - tūemi: i1, kākāriki, poi me kāo
  - tūemi: i6, red, mataono me āe
  - tūemi: i2, kahurangi, koeko me āe
  - tūemi: i3, red, mataono me kāo
  - tūemi: i4, kākāriki, mataono me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - theory2 lý thuyết kích thước 2
  - dự báo vâng nếu: theory0, màu và green
  - dự báo vâng nếu: theory0, hình dáng và nón
  - dự báo vâng nếu: theory1, hình dáng và mặt cầu
  - theory1 lý thuyết kích thước 2
  - theory3 lý thuyết kích thước 4
  - dự báo vâng nếu: theory3, hình dáng và mặt cầu
  - dự báo vâng nếu: theory2, màu và đỏ
  - dự báo vâng nếu: theory3, hình dáng và nón
  - theory0 lý thuyết kích thước 4
Cost model: lệ ngoại cost 4.
Dữ liệu:
  - vật phẩm: i5, green, nón và no
  - vật phẩm: i0, green, nón và vâng
  - vật phẩm: i7, green, mặt cầu và vâng
  - vật phẩm: i1, green, mặt cầu và no
  - vật phẩm: i6, đỏ, lập phương và vâng
  - vật phẩm: i2, blue, nón và vâng
  - vật phẩm: i3, đỏ, lập phương và no
  - vật phẩm: i4, green, lập phương và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - theory2ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory0, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory0, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและทรงกลม
  - theory1ทฤษฎีขนาด2
  - theory3ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory2, สีและแดง
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและกรวย
  - theory0ทฤษฎีขนาด4
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:4.
ข้อมูล:
  - item:i5, เขียว, กรวยและไฮ้
  - item:i0, เขียว, กรวยและใช่
  - item:i7, เขียว, ทรงกลมและใช่
  - item:i1, เขียว, ทรงกลมและไฮ้
  - item:i6, แดง, ลูกบาศก์และใช่
  - item:i2, น้ำเงิน, กรวยและใช่
  - item:i3, แดง, ลูกบาศก์และไฮ้
  - item:i4, เขียว, ลูกบาศก์และใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - theory2 2 theory size
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory0, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory1, shape மற்றும் sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - கணி ஆம் என்றால்: theory3, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory3, shape மற்றும் cone
  - theory0 4 theory size
cost model: exception செலவு 4.
தரவுகள்:
  - item: i5, பச்சை, cone மற்றும் no
  - item: i0, பச்சை, cone மற்றும் ஆம்
  - item: i7, பச்சை, sphere மற்றும் ஆம்
  - item: i1, பச்சை, sphere மற்றும் no
  - item: i6, சிவப்பு, cube மற்றும் ஆம்
  - item: i2, நீலம், cone மற்றும் ஆம்
  - item: i3, சிவப்பு, cube மற்றும் no
  - item: i4, பச்சை, cube மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - theory2 theory size 2
  - predict ndiyo kama: theory0, rangi na kijani
  - predict ndiyo kama: theory0, umbo na koni
  - predict ndiyo kama: theory1, umbo na tufe
  - theory1 theory size 2
  - theory3 theory size 4
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, rangi na ekundu
  - predict ndiyo kama: theory3, umbo na koni
  - theory0 theory size 4
Cost model: exception gharimu: 4.
Data:
  - item: i5, kijani, koni na hapana
  - item: i0, kijani, koni na ndiyo
  - item: i7, kijani, tufe na ndiyo
  - item: i1, kijani, tufe na hapana
  - item: i6, ekundu, mchemraba na ndiyo
  - item: i2, buluu, koni na ndiyo
  - item: i3, ekundu, mchemraba na hapana
  - item: i4, kijani, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - theory2 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory0, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó cone
  - theory0 4 theory size
Cost model: exception bą́ą́h ílį́ 4.
Data:
  - item: i5, dinootłʼizh, cone dóó no
  - item: i0, dinootłʼizh, cone dóó aooʼ
  - item: i7, dinootłʼizh, sphere dóó aooʼ
  - item: i1, dinootłʼizh, sphere dóó no
  - item: i6, łichííʼ, cube dóó aooʼ
  - item: i2, dootłʼizh, cone dóó aooʼ
  - item: i3, łichííʼ, cube dóó no
  - item: i4, dinootłʼizh, cube dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - theory2 2 theory size
  - predict aap if: theory0, qalipaat aamma qorsuk
  - predict aap if: theory0, shape aamma cone
  - predict aap if: theory1, shape aamma sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict aap if: theory3, shape aamma sphere
  - predict aap if: theory2, qalipaat aamma aappalajaarpoq
  - predict aap if: theory3, shape aamma cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, qorsuk, cone aamma no
  - item: i0, qorsuk, cone aamma aap
  - item: i7, qorsuk, sphere aamma aap
  - item: i1, qorsuk, sphere aamma no
  - item: i6, aappalajaarpoq, cube aamma aap
  - item: i2, tungujortoq, cone aamma aap
  - item: i3, aappalajaarpoq, cube aamma no
  - item: i4, qorsuk, cube aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict ya sapos: theory0, kala na grinpela
  - predict ya sapos: theory0, shape na cone
  - predict ya sapos: theory1, shape na sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict ya sapos: theory3, shape na sphere
  - predict ya sapos: theory2, kala na ret
  - predict ya sapos: theory3, shape na cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, grinpela, cone na no
  - item: i0, grinpela, cone na ya
  - item: i7, grinpela, sphere na ya
  - item: i1, grinpela, sphere na no
  - item: i6, ret, cube na ya
  - item: i2, blu, cone na ya
  - item: i3, ret, cube na no
  - item: i4, grinpela, cube na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, color green
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color màxke
  - predict yes if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone yes
  - item: i7, green, sphere yes
  - item: i1, green, sphere no
  - item: i6, màxke, cube yes
  - item: i2, blue, cone yes
  - item: i3, màxke, cube no
  - item: i4, green, cube yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, бос а баьццара
  - predict yes if: theory0, shape а cone
  - predict yes if: theory1, shape а sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape а sphere
  - predict yes if: theory2, бос а цӏе
  - predict yes if: theory3, shape а cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, баьццара, cone а no
  - item: i0, баьццара, cone а yes
  - item: i7, баьццара, sphere а yes
  - item: i1, баьццара, sphere а no
  - item: i6, цӏе, cube а yes
  - item: i2, blue, cone а yes
  - item: i3, цӏе, cube а no
  - item: i4, баьццара, cube а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - theory2 2 이론 크기
  - 예언하다 네 만약: theory0, 색깔 녹색의
  - 예언하다 네 만약: theory0, 모양 솔방울
  - 예언하다 네 만약: theory1, 모양 구체
  - theory1 2 이론 크기
  - theory3 4 이론 크기
  - 예언하다 네 만약: theory3, 모양 구체
  - 예언하다 네 만약: theory2, 색깔 빨갛다
  - 예언하다 네 만약: theory3, 모양 솔방울
  - theory0 4 이론 크기
cost model: 예외 cost 4.
자료:
  - 아이템: i5, 녹색의, 솔방울 아니요
  - 아이템: i0, 녹색의, 솔방울 네
  - 아이템: i7, 녹색의, 구체 네
  - 아이템: i1, 녹색의, 구체 아니요
  - 아이템: i6, 빨갛다, 정육면체 네
  - 아이템: i2, 푸르다, 솔방울 네
  - 아이템: i3, 빨갛다, 정육면체 아니요
  - 아이템: i4, 녹색의, 정육면체 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - theory2 2 theory tamaina
  - aurresan bai if: theory0, kolore eta berde
  - aurresan bai if: theory0, shape eta pinaburu
  - aurresan bai if: theory1, shape eta esfera
  - theory1 2 theory tamaina
  - theory3 4 theory tamaina
  - aurresan bai if: theory3, shape eta esfera
  - aurresan bai if: theory2, kolore eta gorri
  - aurresan bai if: theory3, shape eta pinaburu
  - theory0 4 theory tamaina
Cost model: exception kostatu 4.
Data:
  - item: i5, berde, pinaburu eta no
  - item: i0, berde, pinaburu eta bai
  - item: i7, berde, esfera eta bai
  - item: i1, berde, esfera eta no
  - item: i6, gorri, kubo eta bai
  - item: i2, urdin, pinaburu eta bai
  - item: i3, gorri, kubo eta no
  - item: i4, berde, kubo eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - theory2 theory size 2
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory0, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory1, shape ᎠᎴ sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict ᎥᎥ if: theory3, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory3, shape ᎠᎴ cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᏝᏃ
  - item: i0, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᎥᎥ
  - item: i7, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᎥᎥ
  - item: i1, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᏝᏃ
  - item: i6, ᎩᎦᎨ, cube ᎠᎴ ᎥᎥ
  - item: i2, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᎥᎥ
  - item: i3, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
  - item: i4, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory2 theory chhika 2
  - predict arí if: theory0, llimphi waylla
  - predict arí if: theory0, shape cone
  - predict arí if: theory1, shape sphere
  - theory1 theory chhika 2
  - theory3 theory chhika 4
  - predict arí if: theory3, shape sphere
  - predict arí if: theory2, llimphi puka
  - predict arí if: theory3, shape cone
  - theory0 theory chhika 4
Cost model: exception cost 4.
Data:
  - item: i5, waylla, cone mánan
  - item: i0, waylla, cone arí
  - item: i7, waylla, sphere arí
  - item: i1, waylla, sphere mánan
  - item: i6, puka, cube arí
  - item: i2, anqas, cone arí
  - item: i3, puka, cube mánan
  - item: i4, waylla, cube arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 тиориэ size
  - predict yes if: theory0, color ыкӏи уцышъо
  - predict yes if: theory0, shape ыкӏи cone
  - predict yes if: theory1, shape ыкӏи sphere
  - theory1 2 тиориэ size
  - theory3 4 тиориэ size
  - predict yes if: theory3, shape ыкӏи sphere
  - predict yes if: theory2, color ыкӏи плъыжьы
  - predict yes if: theory3, shape ыкӏи cone
  - theory0 4 тиориэ size
Cost model: exception cost 4.
Data:
  - item: i5, уцышъо, cone ыкӏи no
  - item: i0, уцышъо, cone ыкӏи yes
  - item: i7, уцышъо, sphere ыкӏи yes
  - item: i1, уцышъо, sphere ыкӏи no
  - item: i6, плъыжьы, cube ыкӏи yes
  - item: i2, blue, cone ыкӏи yes
  - item: i3, плъыжьы, cube ыкӏи no
  - item: i4, уцышъо, cube ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, xim thiab ntsuab
  - predict yes if: theory0, shape thiab cone
  - predict yes if: theory1, shape thiab sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape thiab sphere
  - predict yes if: theory2, xim thiab liab
  - predict yes if: theory3, shape thiab cone
  - theory0 theory size 4
Cost model: exception nqi 4.
Data:
  - item: i5, ntsuab, cone thiab no
  - item: i0, ntsuab, cone thiab yes
  - item: i7, ntsuab, sphere thiab yes
  - item: i1, ntsuab, sphere thiab no
  - item: i6, liab, cube thiab yes
  - item: i2, xiav, cone thiab yes
  - item: i3, liab, cube thiab no
  - item: i4, ntsuab, cube thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict ngai if: theory0, color green
  - predict ngai if: theory0, shape cone
  - predict ngai if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict ngai if: theory3, shape sphere
  - predict ngai if: theory2, color red
  - predict ngai if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone woi
  - item: i0, green, cone ngai
  - item: i7, green, sphere ngai
  - item: i1, green, sphere woi
  - item: i6, red, cube ngai
  - item: i2, blue, cone ngai
  - item: i3, red, cube woi
  - item: i4, green, cube ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - theory2 theory size 2
  - predict i̠i if: theory0, color micha' green
  - predict i̠i if: theory0, shape micha' cone
  - predict i̠i if: theory1, shape micha' sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict i̠i if: theory3, shape micha' sphere
  - predict i̠i if: theory2, color micha' homa
  - predict i̠i if: theory3, shape micha' cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone micha' no
  - item: i0, green, cone micha' i̠i
  - item: i7, green, sphere micha' i̠i
  - item: i1, green, sphere micha' no
  - item: i6, homa, cube micha' i̠i
  - item: i2, blue, cone micha' i̠i
  - item: i3, homa, cube micha' no
  - item: i4, green, cube micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory0, color カ green
  - predict yes if: theory0, shape カ cone
  - predict yes if: theory1, shape カ sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, shape カ sphere
  - predict yes if: theory2, color カ フレ
  - predict yes if: theory3, shape カ cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, green, cone カ ソモ
  - item: i0, green, cone カ yes
  - item: i7, green, sphere カ yes
  - item: i1, green, sphere カ ソモ
  - item: i6, フレ, cube カ yes
  - item: i2, blue, cone カ yes
  - item: i3, フレ, cube カ ソモ
  - item: i4, green, cube カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes эвыр: theory0, пэран ынкъам green
  - predict yes эвыр: theory0, shape ынкъам cone
  - predict yes эвыр: theory1, shape ынкъам sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes эвыр: theory3, shape ынкъам sphere
  - predict yes эвыр: theory2, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory3, shape ынкъам cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone ынкъам no
  - item: i0, green, cone ынкъам yes
  - item: i7, green, sphere ынкъам yes
  - item: i1, green, sphere ынкъам no
  - item: i6, чеԓгыԓьын, cube ынкъам yes
  - item: i2, ныԓиԓиԓӄин, cone ынкъам yes
  - item: i3, чеԓгыԓьын, cube ынкъам no
  - item: i4, green, cube ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes wa: theory0, color yéetel yaʼax
  - predict yes wa: theory0, shape yéetel cone
  - predict yes wa: theory1, shape yéetel sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes wa: theory3, shape yéetel sphere
  - predict yes wa: theory2, color yéetel chak
  - predict yes wa: theory3, shape yéetel cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, yaʼax, cone yéetel no
  - item: i0, yaʼax, cone yéetel yes
  - item: i7, yaʼax, sphere yéetel yes
  - item: i1, yaʼax, sphere yéetel no
  - item: i6, chak, cube yéetel yes
  - item: i2, blue, cone yéetel yes
  - item: i3, chak, cube yéetel no
  - item: i4, yaʼax, cube yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict hą if: theory0, color green
  - predict hą if: theory0, shape cone
  - predict hą if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict hą if: theory3, shape sphere
  - predict hą if: theory2, color red
  - predict hą if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone hą
  - item: i7, green, sphere hą
  - item: i1, green, sphere no
  - item: i6, red, cube hą
  - item: i2, blue, cone hą
  - item: i3, red, cube no
  - item: i4, green, cube hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory0, color na green
  - predict yes if: theory0, shape na cone
  - predict yes if: theory1, shape na sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, shape na sphere
  - predict yes if: theory2, color na red
  - predict yes if: theory3, shape na cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, green, cone na no
  - item: i0, green, cone na yes
  - item: i7, green, sphere na yes
  - item: i1, green, sphere na no
  - item: i6, red, cube na yes
  - item: i2, blue, cone na yes
  - item: i3, red, cube na no
  - item: i4, green, cube na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict heẽ if: theory0, color green
  - predict heẽ if: theory0, shape cone
  - predict heẽ if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict heẽ if: theory3, shape sphere
  - predict heẽ if: theory2, color pytã
  - predict heẽ if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone nahániri
  - item: i0, green, cone heẽ
  - item: i7, green, sphere heẽ
  - item: i1, green, sphere nahániri
  - item: i6, pytã, cube heẽ
  - item: i2, blue, cone heẽ
  - item: i3, pytã, cube nahániri
  - item: i4, green, cube heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict jisa if: theory0, sami ch’uxña
  - predict jisa if: theory0, shape cone
  - predict jisa if: theory1, shape sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict jisa if: theory3, shape sphere
  - predict jisa if: theory2, sami chupika
  - predict jisa if: theory3, shape cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, ch’uxña, cone no
  - item: i0, ch’uxña, cone jisa
  - item: i7, ch’uxña, sphere jisa
  - item: i1, ch’uxña, sphere no
  - item: i6, chupika, cube jisa
  - item: i2, larama, cone jisa
  - item: i3, chupika, cube no
  - item: i4, ch’uxña, cube jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory ʻǁnàhã 2
  - predict ʻée if: theory0, color ǂʻá green
  - predict ʻée if: theory0, shape ǂʻá cone
  - predict ʻée if: theory1, shape ǂʻá sphere
  - theory1 theory ʻǁnàhã 2
  - theory3 theory ʻǁnàhã 4
  - predict ʻée if: theory3, shape ǂʻá sphere
  - predict ʻée if: theory2, color ǂʻá red
  - predict ʻée if: theory3, shape ǂʻá cone
  - theory0 theory ʻǁnàhã 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone ǂʻá ʻĩĩ
  - item: i0, green, cone ǂʻá ʻée
  - item: i7, green, sphere ǂʻá ʻée
  - item: i1, green, sphere ǂʻá ʻĩĩ
  - item: i6, red, cube ǂʻá ʻée
  - item: i2, blue, cone ǂʻá ʻée
  - item: i3, red, cube ǂʻá ʻĩĩ
  - item: i4, green, cube ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory0, вал green
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, вал red
  - predict yes if: theory3, shape cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone yes
  - item: i7, green, sphere yes
  - item: i1, green, sphere no
  - item: i6, red, cube yes
  - item: i2, blue, cone yes
  - item: i3, red, cube no
  - item: i4, green, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, color green
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone yes
  - item: i7, green, sphere yes
  - item: i1, green, sphere no
  - item: i6, red, cube yes
  - item: i2, blue, cone yes
  - item: i3, red, cube no
  - item: i4, green, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory0, color green
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone yes
  - item: i7, green, sphere yes
  - item: i1, green, sphere no
  - item: i6, red, cube yes
  - item: i2, blue, cone yes
  - item: i3, red, cube no
  - item: i4, green, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict ee if: theory0, color green
  - predict ee if: theory0, shape cone
  - predict ee if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict ee if: theory3, shape sphere
  - predict ee if: theory2, color red
  - predict ee if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone ee
  - item: i7, green, sphere ee
  - item: i1, green, sphere no
  - item: i6, red, cube ee
  - item: i2, blue, cone ee
  - item: i3, red, cube no
  - item: i4, green, cube ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, color green
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone inte
  - item: i0, green, cone yes
  - item: i7, green, sphere yes
  - item: i1, green, sphere inte
  - item: i6, red, cube yes
  - item: i2, blue, cone yes
  - item: i3, red, cube inte
  - item: i4, green, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - theory2 théorie taille 2
  - prédire oui si: theory0, couleur et vert
  - prédire oui si: theory0, forme et cône
  - prédire oui si: theory1, forme et sphère
  - theory1 théorie taille 2
  - theory3 théorie taille 4
  - prédire oui si: theory3, forme et sphère
  - prédire oui si: theory2, couleur et rouge
  - prédire oui si: theory3, forme et cône
  - theory0 théorie taille 4
Coûts : exception coûter 4.
Les données sont:
  - article: i5, vert, cône et no
  - article: i0, vert, cône et oui
  - article: i7, vert, sphère et oui
  - article: i1, vert, sphère et no
  - article: i6, rouge, cube et oui
  - article: i2, bleu, cône et oui
  - article: i3, rouge, cube et no
  - article: i4, vert, cube et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - theory2 teorio grandeco 2
  - predict jes se: theory0, koloro kaj verda
  - predict jes se: theory0, formi kaj strobilo
  - predict jes se: theory1, formi kaj sfero
  - theory1 teorio grandeco 2
  - theory3 teorio grandeco 4
  - predict jes se: theory3, formi kaj sfero
  - predict jes se: theory2, koloro kaj ruĝa
  - predict jes se: theory3, formi kaj strobilo
  - theory0 teorio grandeco 4
Kosto pozistino: escepto kosti 4.
Datumo:
  - ero: i5, verda, strobilo kaj no
  - ero: i0, verda, strobilo kaj jes
  - ero: i7, verda, sfero kaj jes
  - ero: i1, verda, sfero kaj no
  - ero: i6, ruĝa, kubo kaj jes
  - ero: i2, blua, strobilo kaj jes
  - ero: i3, ruĝa, kubo kaj no
  - ero: i4, verda, kubo kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - theory2 elmélet méret 2
  - megjósol igen ha: theory0, szín és zöld
  - megjósol igen ha: theory0, alak és toboz
  - megjósol igen ha: theory1, alak és gömb
  - theory1 elmélet méret 2
  - theory3 elmélet méret 4
  - megjósol igen ha: theory3, alak és gömb
  - megjósol igen ha: theory2, szín és piros
  - megjósol igen ha: theory3, alak és toboz
  - theory0 elmélet méret 4
Költség manöken: kivétel kerül 4.
Adat:
  - cikk: i5, zöld, toboz és no
  - cikk: i0, zöld, toboz és igen
  - cikk: i7, zöld, gömb és igen
  - cikk: i1, zöld, gömb és no
  - cikk: i6, piros, kocka és igen
  - cikk: i2, kék, toboz és igen
  - cikk: i3, piros, kocka és no
  - cikk: i4, zöld, kocka és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - theory2theory size2
  - predict係要是:theory0, 色及綠色嘅
  - predict係要是:theory0, 形狀及cone
  - predict係要是:theory1, 形狀及sphere
  - theory1theory size2
  - theory3theory size4
  - predict係要是:theory3, 形狀及sphere
  - predict係要是:theory2, 色及紅
  - predict係要是:theory3, 形狀及cone
  - theory0theory size4
cost model:exception cost:4.
數據:
  - item:i5, 綠色嘅, cone及no
  - item:i0, 綠色嘅, cone及係
  - item:i7, 綠色嘅, sphere及係
  - item:i1, 綠色嘅, sphere及no
  - item:i6, 紅, cube及係
  - item:i2, 藍色嘅, cone及係
  - item:i3, 紅, cube及no
  - item:i4, 綠色嘅, cube及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, color とぅ green
  - predict yes if: theory0, shape とぅ cone
  - predict yes if: theory1, shape とぅ sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape とぅ sphere
  - predict yes if: theory2, color とぅ red
  - predict yes if: theory3, shape とぅ cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone とぅ no
  - item: i0, green, cone とぅ yes
  - item: i7, green, sphere とぅ yes
  - item: i1, green, sphere とぅ no
  - item: i6, red, cube とぅ yes
  - item: i2, blue, cone とぅ yes
  - item: i3, red, cube とぅ no
  - item: i4, green, cube とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - theory2 теория мөлшер 2
  - predict иә егер: theory0, түс және жасыл
  - predict иә егер: theory0, нысан және бүршік
  - predict иә егер: theory1, нысан және сфера
  - theory1 теория мөлшер 2
  - theory3 теория мөлшер 4
  - predict иә егер: theory3, нысан және сфера
  - predict иә егер: theory2, түс және қызыл
  - predict иә егер: theory3, нысан және бүршік
  - theory0 теория мөлшер 4
Тұру үлгі: ерекшелік тұру 4.
Деректер:
  - зат: i5, жасыл, бүршік және жоқ
  - зат: i0, жасыл, бүршік және иә
  - зат: i7, жасыл, сфера және иә
  - зат: i1, жасыл, сфера және жоқ
  - зат: i6, қызыл, куб және иә
  - зат: i2, көк, бүршік және иә
  - зат: i3, қызыл, куб және жоқ
  - зат: i4, жасыл, куб және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - theory2 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory0, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory0, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory1, צוּרָה וְ־ כַּדּוּר
  - theory1 תּוֹרָה גֹּדֶל 2
  - theory3 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory3, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory2, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory3, צוּרָה וְ־ אצטרובל
  - theory0 תּוֹרָה גֹּדֶל 4
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 4.
נְתוּנִים:
  - אבזר: i5, ירוק מקנאה, אצטרובל וְ־ no
  - אבזר: i0, ירוק מקנאה, אצטרובל וְ־ כֵּן
  - אבזר: i7, ירוק מקנאה, כַּדּוּר וְ־ כֵּן
  - אבזר: i1, ירוק מקנאה, כַּדּוּר וְ־ no
  - אבזר: i6, אָדוֹם, קובייה וְ־ כֵּן
  - אבזר: i2, כָּחוֹל, אצטרובל וְ־ כֵּן
  - אבזר: i3, אָדוֹם, קובייה וְ־ no
  - אבזר: i4, ירוק מקנאה, קובייה וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - theory2 theory size 2
  - predict იახ if: theory0, color დო green
  - predict იახ if: theory0, shape დო cone
  - predict იახ if: theory1, shape დო sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict იახ if: theory3, shape დო sphere
  - predict იახ if: theory2, color დო red
  - predict იახ if: theory3, shape დო cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone დო no
  - item: i0, green, cone დო იახ
  - item: i7, green, sphere დო იახ
  - item: i1, green, sphere დო no
  - item: i6, red, cube დო იახ
  - item: i2, blue, cone დო იახ
  - item: i3, red, cube დო no
  - item: i4, green, cube დო იახ
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - theory2 teori ukuran 2
  - predict ya jika: theory0, warna dan hijau
  - predict ya jika: theory0, bentuk dan kerucut
  - predict ya jika: theory1, bentuk dan bola
  - theory1 teori ukuran 2
  - theory3 teori ukuran 4
  - predict ya jika: theory3, bentuk dan bola
  - predict ya jika: theory2, warna dan merah
  - predict ya jika: theory3, bentuk dan kerucut
  - theory0 teori ukuran 4
Cost model: pengecualian menghabiskan biaya 4.
Data:
  - benda: i5, hijau, kerucut dan no
  - benda: i0, hijau, kerucut dan ya
  - benda: i7, hijau, bola dan ya
  - benda: i1, hijau, bola dan no
  - benda: i6, merah, kubus dan ya
  - benda: i2, biru, kerucut dan ya
  - benda: i3, merah, kubus dan no
  - benda: i4, hijau, kubus dan ya
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - theory2ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory0, គ្រោងនឹងកោណ
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងគោល
  - theory1ទ្រឹស្ដីទំហំ2
  - theory3ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងកោណ
  - theory0ទ្រឹស្ដីទំហំ4
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:4.
ទិន្នន័យ:
  - item:i5, បៃតង, កោណនឹងទេ
  - item:i0, បៃតង, កោណនឹងច៎ា
  - item:i7, បៃតង, គោលនឹងច៎ា
  - item:i1, បៃតង, គោលនឹងទេ
  - item:i6, ក្រហម, គូបនឹងច៎ា
  - item:i2, ខៀវ, កោណនឹងច៎ា
  - item:i3, ក្រហម, គូបនឹងទេ
  - item:i4, បៃតង, គូបនឹងច៎ា
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - theory2ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ຮູບຮ່າງແລະcone
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະຄະດີ
  - theory1ທິດສະດີຂະຫນາດ2
  - theory3ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະcone
  - theory0ທິດສະດີຂະຫນາດ4
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:4.
ຂໍ້ມູນ:
  - item:i5, ຂຽວ, coneແລະno
  - item:i0, ຂຽວ, coneແລະແມ່ນ
  - item:i7, ຂຽວ, ຄະດີແລະແມ່ນ
  - item:i1, ຂຽວ, ຄະດີແລະno
  - item:i6, ແດງ, ລູກບາດແລະແມ່ນ
  - item:i2, ຟ້າ, coneແລະແມ່ນ
  - item:i3, ແດງ, ລູກບາດແລະno
  - item:i4, ຂຽວ, ລູກບາດແລະແມ່ນ
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - theory2 2 theory size
  - predict అవును అయితే: theory0, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory0, ఆకారం మరియు cone
  - predict అవును అయితే: theory1, ఆకారం మరియు గోళము
  - theory1 2 theory size
  - theory3 4 theory size
  - predict అవును అయితే: theory3, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory2, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory3, ఆకారం మరియు cone
  - theory0 4 theory size
cost model: exception cost 4.
data:
  - item: i5, పచ్చని, cone మరియు no
  - item: i0, పచ్చని, cone మరియు అవును
  - item: i7, పచ్చని, గోళము మరియు అవును
  - item: i1, పచ్చని, గోళము మరియు no
  - item: i6, ఎర్ర, cube మరియు అవును
  - item: i2, నీలము, cone మరియు అవును
  - item: i3, ఎర్ర, cube మరియు no
  - item: i4, పచ్చని, cube మరియు అవును
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - theory2 theory size 2
  - predict hẹn if: theory0, color green
  - predict hẹn if: theory0, shape cone
  - predict hẹn if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict hẹn if: theory3, shape sphere
  - predict hẹn if: theory2, color pupa
  - predict hẹn if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone rárá
  - item: i0, green, cone bẹ́ẹ̀ ni
  - item: i7, green, sphere bẹ́ẹ̀ ni
  - item: i1, green, sphere rárá
  - item: i6, pupa, cube bẹ́ẹ̀ ni
  - item: i2, dúdú, cone bẹ́ẹ̀ ni
  - item: i3, pupa, cube rárá
  - item: i4, green, cube bẹ́ẹ̀ ni
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory2 2 theory size
  - predict au if: theory0, color green
  - predict au if: theory0, shape cone
  - predict au if: theory1, shape sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict au if: theory3, shape sphere
  - predict au if: theory2, color łichíí
  - predict au if: theory3, shape cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone au
  - item: i7, green, sphere au
  - item: i1, green, sphere no
  - item: i6, łichíí, cube au
  - item: i2, blue, cone au
  - item: i3, łichíí, cube no
  - item: i4, green, cube au
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - theory2 theory size 2
  - predict yes if: theory0, color green
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape cone
  - theory0 theory size 4
cost model: exception cost 4.
data:
  - item: i5, green, cone ᐋᒃᑲ
  - item: i0, green, cone yes
  - item: i7, green, sphere yes
  - item: i1, green, sphere ᐋᒃᑲ
  - item: i6, red, cube yes
  - item: i2, blue, cone yes
  - item: i3, red, cube ᐋᒃᑲ
  - item: i4, green, cube yes
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, color grun
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, grun, cone no
  - item: i0, grun, cone yes
  - item: i7, grun, sphere yes
  - item: i1, grun, sphere no
  - item: i6, red, cube yes
  - item: i2, blaw, cone yes
  - item: i3, red, cube no
  - item: i4, grun, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory0, color green
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape cone
  - theory0 theory size 4
Cost model: exception cost 4.
Data:
  - item: i5, green, cone no
  - item: i0, green, cone yes
  - item: i7, green, sphere yes
  - item: i1, green, sphere no
  - item: i6, red, cube yes
  - item: i2, blue, cone yes
  - item: i3, red, cube no
  - item: i4, green, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory0, ранг къацу
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, shape sphere
  - theory1 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, ранг red
  - predict yes if: theory3, shape cone
  - theory0 4 theory size
Cost model: exception cost 4.
Data:
  - item: i5, къацу, cone no
  - item: i0, къацу, cone yes
  - item: i7, къацу, sphere yes
  - item: i1, къацу, sphere no
  - item: i6, red, cube yes
  - item: i2, blue, cone yes
  - item: i3, red, cube no
  - item: i4, къацу, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - theory2 theory size 2
  - theory3 theory size 4
  - predicts yes if: theory3, colour and green
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory1, colour and blue
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, shape and sphere
  - theory1 theory size 2
  - theory0 theory size 2
Costs: exception cost 2.
The data are:
  - item: i0, red, cube and no
  - item: i1, red, sphere and no
  - item: i5, red, cone and yes
  - item: i7, green, cube and no
  - item: i4, blue, cone and no
  - item: i6, red, cone and no
  - item: i3, green, sphere and no
  - item: i2, red, cone and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - theory2 el tamaño de la teoría 2
  - theory3 el tamaño de la teoría 4
  - predice sí si: theory3, es de color y verde
  - predice sí si: theory0, es de color y rojo
  - predice sí si: theory1, es de color y azul
  - predice sí si: theory3, forma y esfera
  - predice sí si: theory2, forma y esfera
  - theory1 el tamaño de la teoría 2
  - theory0 el tamaño de la teoría 2
Costes: exception el coste: 2.
Los datos son:
  - el elemento: i0, rojo, cubo y no
  - el elemento: i1, rojo, esfera y no
  - el elemento: i5, rojo, cono y sí
  - el elemento: i7, verde, cubo y no
  - el elemento: i4, azul, cono y no
  - el elemento: i6, rojo, cono y no
  - el elemento: i3, verde, esfera y no
  - el elemento: i2, rojo, cono y no
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - theory2的理论规模是2
  - theory3的理论规模是4
  - 预测是若：theory3、颜色和绿色的
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory1、颜色和蓝色的
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、形状和球体
  - theory1的理论规模是2
  - theory0的理论规模是2
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i0、红色的、立方体和否
  - 的物品是：i1、红色的、球体和否
  - 的物品是：i5、红色的、圆锥和是
  - 的物品是：i7、绿色的、立方体和否
  - 的物品是：i4、蓝色的、圆锥和否
  - 的物品是：i6、红色的、圆锥和否
  - 的物品是：i3、绿色的、球体和否
  - 的物品是：i2、红色的、圆锥和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - theory2 2 kuram boyut
  - theory3 4 kuram boyut
  - öngörür evet eğer: theory3, renk ve yeşil
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory1, renk ve mavi
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, şekil ve küre
  - theory1 2 kuram boyut
  - theory0 2 kuram boyut
Cost model: exception maliyet: 2.
Veriler:
  - item: i0, kırmızı, küp ve hayır
  - item: i1, kırmızı, küre ve hayır
  - item: i5, kırmızı, koni ve evet
  - item: i7, yeşil, küp ve hayır
  - item: i4, mavi, koni ve hayır
  - item: i6, kırmızı, koni ve hayır
  - item: i3, yeşil, küre ve hayır
  - item: i2, kırmızı, koni ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict ndiyo kama: theory3, rangi na kijani
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory1, rangi na buluu
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, umbo na tufe
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception gharimu: 2.
Data:
  - item: i0, ekundu, mchemraba na hapana
  - item: i1, ekundu, tufe na hapana
  - item: i5, ekundu, koni na ndiyo
  - item: i7, kijani, mchemraba na hapana
  - item: i4, buluu, koni na hapana
  - item: i6, ekundu, koni na hapana
  - item: i3, kijani, tufe na hapana
  - item: i2, ekundu, koni na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - theory2 theory size 2
  - theory3 theory size 4
  - predicts yes if: theory3, colour and green
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory1, colour and blue
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, shape and sphere
  - theory1 theory size 2
  - theory0 theory size 2
Costs: exception cost 2.
The data are:
  - item: i0, red, cube and no
  - item: i1, red, sphere and no
  - item: i5, red, cone and yes
  - item: i7, green, cube and no
  - item: i4, blue, cone and no
  - item: i6, red, cone and no
  - item: i3, green, sphere and no
  - item: i2, red, cone and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(theory_size theory2 2) (theory_size theory3 4) (predicts_yes_if theory3 color green) (predicts_yes_if theory0 color red) (predicts_yes_if theory1 color blue) (predicts_yes_if theory3 shape sphere) (predicts_yes_if theory2 shape sphere) (theory_size theory1 2) (theory_size theory0 2)], cost_model: [(exception_cost 2)], data: [(item i0 red cube no) (item i1 red sphere no) (item i5 red cone yes) (item i7 green cube no) (item i4 blue cone no) (item i6 red cone no) (item i3 green sphere no) (item i2 red cone no)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - theory2 Theorie Größe 2
  - theory3 Theorie Größe 4
  - vorhersagen ja wenn: theory3, Farbe und grün
  - vorhersagen ja wenn: theory0, Farbe und rot
  - vorhersagen ja wenn: theory1, Farbe und blau
  - vorhersagen ja wenn: theory3, Form und Kugel
  - vorhersagen ja wenn: theory2, Form und Kugel
  - theory1 Theorie Größe 2
  - theory0 Theorie Größe 2
Kosten: Ausnahme kosten 2.
Die Daten sind:
  - Ding: i0, rot, Kubus und nein
  - Ding: i1, rot, Kugel und nein
  - Ding: i5, rot, Kegel und ja
  - Ding: i7, grün, Kubus und nein
  - Ding: i4, blau, Kegel und nein
  - Ding: i6, rot, Kegel und nein
  - Ding: i3, grün, Kugel und nein
  - Ding: i2, rot, Kegel und nein
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - theory2 тѐо̄рија величѝна 2
  - theory3 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory3, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory0, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory1, бо̀ја и пла̑в
  - predvidjeti да ȁko: theory3, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory2, о̏блӣк и сфе́ра
  - theory1 тѐо̄рија величѝна 2
  - theory0 тѐо̄рија величѝна 2
Trošak мо̀дел: изузе́так ко̏штати 2.
Податци:
  - item: i0, цр̀вен, коцка и no
  - item: i1, цр̀вен, сфе́ра и no
  - item: i5, цр̀вен, stožac и да
  - item: i7, зѐлен, коцка и no
  - item: i4, пла̑в, stožac и no
  - item: i6, цр̀вен, stožac и no
  - item: i3, зѐлен, сфе́ра и no
  - item: i2, цр̀вен, stožac и no
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - theory2 teoria koko 2
  - theory3 teoria koko 4
  - ennakoida kyllä jos: theory3, väri ja vihreä
  - ennakoida kyllä jos: theory0, väri ja punainen
  - ennakoida kyllä jos: theory1, väri ja sininen
  - ennakoida kyllä jos: theory3, muoto ja pallo
  - ennakoida kyllä jos: theory2, muoto ja pallo
  - theory1 teoria koko 2
  - theory0 teoria koko 2
Kustannus malli: poikkeus maksaa 2.
Tieto:
  - esine: i0, punainen, kuutio ja ei
  - esine: i1, punainen, pallo ja ei
  - esine: i5, punainen, kartio ja kyllä
  - esine: i7, vihreä, kuutio ja ei
  - esine: i4, sininen, kartio ja ei
  - esine: i6, punainen, kartio ja ei
  - esine: i3, vihreä, pallo ja ei
  - esine: i2, punainen, kartio ja ei
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - theory2的理论规模是2
  - theory3的理论规模是4
  - 预测是若：theory3、颜色和绿色的
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory1、颜色和蓝色的
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、形状和球体
  - theory1的理论规模是2
  - theory0的理论规模是2
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i0、红色的、立方体和否
  - 的物品是：i1、红色的、球体和否
  - 的物品是：i5、红色的、圆锥和是
  - 的物品是：i7、绿色的、立方体和否
  - 的物品是：i4、蓝色的、圆锥和否
  - 的物品是：i6、红色的、圆锥和否
  - 的物品是：i3、绿色的、球体和否
  - 的物品是：i2、红色的、圆锥和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - theory22理論大きさ
  - theory34理論大きさ
  - 予言するはいもし...:theory3, 色と緑の
  - 予言するはいもし...:theory0, 色と赤い
  - 予言するはいもし...:theory1, 色と青い
  - 予言するはいもし...:theory3, 形と球
  - 予言するはいもし...:theory2, 形と球
  - theory12理論大きさ
  - theory02理論大きさ
費用モデル:例外費やす:2.
資料:
  - 項目:i0, 赤い, 立方体といいえ
  - 項目:i1, 赤い, 球といいえ
  - 項目:i5, 赤い, 松毬とはい
  - 項目:i7, 緑の, 立方体といいえ
  - 項目:i4, 青い, 松毬といいえ
  - 項目:i6, 赤い, 松毬といいえ
  - 項目:i3, 緑の, 球といいえ
  - 項目:i2, 赤い, 松毬といいえ
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - theory2 2 kuram boyut
  - theory3 4 kuram boyut
  - öngörür evet eğer: theory3, renk ve yeşil
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory1, renk ve mavi
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, şekil ve küre
  - theory1 2 kuram boyut
  - theory0 2 kuram boyut
Cost model: exception maliyet: 2.
Veriler:
  - item: i0, kırmızı, küp ve hayır
  - item: i1, kırmızı, küre ve hayır
  - item: i5, kırmızı, koni ve evet
  - item: i7, yeşil, küp ve hayır
  - item: i4, mavi, koni ve hayır
  - item: i6, kırmızı, koni ve hayır
  - item: i3, yeşil, küre ve hayır
  - item: i2, kırmızı, koni ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - نَظَرِيَّة مَقَاس theory2 2
  - نَظَرِيَّة مَقَاس theory3 4
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَزْرَق
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ كُرَة
  - نَظَرِيَّة مَقَاس theory1 2
  - نَظَرِيَّة مَقَاس theory0 2
cost model: اِسْتِثْنَاء cost 2.
بَيَانَات:
  - بَنْد: i0، أَحْمَر، كَعْبَة وَـ no
  - بَنْد: i1، أَحْمَر، كُرَة وَـ no
  - بَنْد: i5، أَحْمَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i7، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i4، أَزْرَق، مَخْرُوط وَـ no
  - بَنْد: i6، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i3، أَخْضَر، كُرَة وَـ no
  - بَنْد: i2، أَحْمَر، مَخْرُوط وَـ no
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - theory2 2 თეორია ზომა
  - theory3 4 თეორია ზომა
  - predict დიახ თუ: theory3, ფერი და მწვანე
  - predict დიახ თუ: theory0, ფერი და წითელი
  - predict დიახ თუ: theory1, ფერი და ლურჯი
  - predict დიახ თუ: theory3, ფორმა და სფერო
  - predict დიახ თუ: theory2, ფორმა და სფერო
  - theory1 2 თეორია ზომა
  - theory0 2 თეორია ზომა
Ფასი მოდელი: გამონაკლისი ღირს 2.
Მონაცემები:
  - item: i0, წითელი, კუბი და no
  - item: i1, წითელი, სფერო და no
  - item: i5, წითელი, გირჩა და დიახ
  - item: i7, მწვანე, კუბი და no
  - item: i4, ლურჯი, გირჩა და no
  - item: i6, წითელი, გირჩა და no
  - item: i3, მწვანე, სფერო და no
  - item: i2, წითელი, გირჩა და no
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - ariā size theory2 2
  - ariā size theory3 4
  - matakite āe mēnā: theory3, kano me kākāriki
  - matakite āe mēnā: theory0, kano me red
  - matakite āe mēnā: theory1, kano me kahurangi
  - matakite āe mēnā: theory3, āhuahanga me poi
  - matakite āe mēnā: theory2, āhuahanga me poi
  - ariā size theory1 2
  - ariā size theory0 2
Cost model: hāungatanga cost 2.
Hōtuku:
  - tūemi: i0, red, mataono me kāo
  - tūemi: i1, red, poi me kāo
  - tūemi: i5, red, koeko me āe
  - tūemi: i7, kākāriki, mataono me kāo
  - tūemi: i4, kahurangi, koeko me kāo
  - tūemi: i6, red, koeko me kāo
  - tūemi: i3, kākāriki, poi me kāo
  - tūemi: i2, red, koeko me kāo
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - theory2 lý thuyết kích thước 2
  - theory3 lý thuyết kích thước 4
  - dự báo vâng nếu: theory3, màu và green
  - dự báo vâng nếu: theory0, màu và đỏ
  - dự báo vâng nếu: theory1, màu và blue
  - dự báo vâng nếu: theory3, hình dáng và mặt cầu
  - dự báo vâng nếu: theory2, hình dáng và mặt cầu
  - theory1 lý thuyết kích thước 2
  - theory0 lý thuyết kích thước 2
Cost model: lệ ngoại cost 2.
Dữ liệu:
  - vật phẩm: i0, đỏ, lập phương và no
  - vật phẩm: i1, đỏ, mặt cầu và no
  - vật phẩm: i5, đỏ, nón và vâng
  - vật phẩm: i7, green, lập phương và no
  - vật phẩm: i4, blue, nón và no
  - vật phẩm: i6, đỏ, nón và no
  - vật phẩm: i3, green, mặt cầu và no
  - vật phẩm: i2, đỏ, nón và no
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - theory2ทฤษฎีขนาด2
  - theory3ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory3, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory0, สีและแดง
  - ทำนายใช่ถ้า หาก:theory1, สีและน้ำเงิน
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและทรงกลม
  - theory1ทฤษฎีขนาด2
  - theory0ทฤษฎีขนาด2
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:2.
ข้อมูล:
  - item:i0, แดง, ลูกบาศก์และไฮ้
  - item:i1, แดง, ทรงกลมและไฮ้
  - item:i5, แดง, กรวยและใช่
  - item:i7, เขียว, ลูกบาศก์และไฮ้
  - item:i4, น้ำเงิน, กรวยและไฮ้
  - item:i6, แดง, กรวยและไฮ้
  - item:i3, เขียว, ทรงกลมและไฮ้
  - item:i2, แดง, กรวยและไฮ้
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - theory2 2 theory size
  - theory3 4 theory size
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் நீலம்
  - கணி ஆம் என்றால்: theory3, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory2, shape மற்றும் sphere
  - theory1 2 theory size
  - theory0 2 theory size
cost model: exception செலவு 2.
தரவுகள்:
  - item: i0, சிவப்பு, cube மற்றும் no
  - item: i1, சிவப்பு, sphere மற்றும் no
  - item: i5, சிவப்பு, cone மற்றும் ஆம்
  - item: i7, பச்சை, cube மற்றும் no
  - item: i4, நீலம், cone மற்றும் no
  - item: i6, சிவப்பு, cone மற்றும் no
  - item: i3, பச்சை, sphere மற்றும் no
  - item: i2, சிவப்பு, cone மற்றும் no
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict ndiyo kama: theory3, rangi na kijani
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory1, rangi na buluu
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, umbo na tufe
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception gharimu: 2.
Data:
  - item: i0, ekundu, mchemraba na hapana
  - item: i1, ekundu, tufe na hapana
  - item: i5, ekundu, koni na ndiyo
  - item: i7, kijani, mchemraba na hapana
  - item: i4, buluu, koni na hapana
  - item: i6, ekundu, koni na hapana
  - item: i3, kijani, tufe na hapana
  - item: i2, ekundu, koni na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception bą́ą́h ílį́ 2.
Data:
  - item: i0, łichííʼ, cube dóó no
  - item: i1, łichííʼ, sphere dóó no
  - item: i5, łichííʼ, cone dóó aooʼ
  - item: i7, dinootłʼizh, cube dóó no
  - item: i4, dootłʼizh, cone dóó no
  - item: i6, łichííʼ, cone dóó no
  - item: i3, dinootłʼizh, sphere dóó no
  - item: i2, łichííʼ, cone dóó no
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict aap if: theory3, qalipaat aamma qorsuk
  - predict aap if: theory0, qalipaat aamma aappalajaarpoq
  - predict aap if: theory1, qalipaat aamma tungujortoq
  - predict aap if: theory3, shape aamma sphere
  - predict aap if: theory2, shape aamma sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, aappalajaarpoq, cube aamma no
  - item: i1, aappalajaarpoq, sphere aamma no
  - item: i5, aappalajaarpoq, cone aamma aap
  - item: i7, qorsuk, cube aamma no
  - item: i4, tungujortoq, cone aamma no
  - item: i6, aappalajaarpoq, cone aamma no
  - item: i3, qorsuk, sphere aamma no
  - item: i2, aappalajaarpoq, cone aamma no
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict ya sapos: theory3, kala na grinpela
  - predict ya sapos: theory0, kala na ret
  - predict ya sapos: theory1, kala na blu
  - predict ya sapos: theory3, shape na sphere
  - predict ya sapos: theory2, shape na sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, ret, cube na no
  - item: i1, ret, sphere na no
  - item: i5, ret, cone na ya
  - item: i7, grinpela, cube na no
  - item: i4, blu, cone na no
  - item: i6, ret, cone na no
  - item: i3, grinpela, sphere na no
  - item: i2, ret, cone na no
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, color green
  - predict yes if: theory0, color màxke
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, màxke, cube no
  - item: i1, màxke, sphere no
  - item: i5, màxke, cone yes
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, màxke, cone no
  - item: i3, green, sphere no
  - item: i2, màxke, cone no
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, бос а баьццара
  - predict yes if: theory0, бос а цӏе
  - predict yes if: theory1, бос а blue
  - predict yes if: theory3, shape а sphere
  - predict yes if: theory2, shape а sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, цӏе, cube а no
  - item: i1, цӏе, sphere а no
  - item: i5, цӏе, cone а yes
  - item: i7, баьццара, cube а no
  - item: i4, blue, cone а no
  - item: i6, цӏе, cone а no
  - item: i3, баьццара, sphere а no
  - item: i2, цӏе, cone а no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - theory2 2 이론 크기
  - theory3 4 이론 크기
  - 예언하다 네 만약: theory3, 색깔 녹색의
  - 예언하다 네 만약: theory0, 색깔 빨갛다
  - 예언하다 네 만약: theory1, 색깔 푸르다
  - 예언하다 네 만약: theory3, 모양 구체
  - 예언하다 네 만약: theory2, 모양 구체
  - theory1 2 이론 크기
  - theory0 2 이론 크기
cost model: 예외 cost 2.
자료:
  - 아이템: i0, 빨갛다, 정육면체 아니요
  - 아이템: i1, 빨갛다, 구체 아니요
  - 아이템: i5, 빨갛다, 솔방울 네
  - 아이템: i7, 녹색의, 정육면체 아니요
  - 아이템: i4, 푸르다, 솔방울 아니요
  - 아이템: i6, 빨갛다, 솔방울 아니요
  - 아이템: i3, 녹색의, 구체 아니요
  - 아이템: i2, 빨갛다, 솔방울 아니요
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - theory2 2 theory tamaina
  - theory3 4 theory tamaina
  - aurresan bai if: theory3, kolore eta berde
  - aurresan bai if: theory0, kolore eta gorri
  - aurresan bai if: theory1, kolore eta urdin
  - aurresan bai if: theory3, shape eta esfera
  - aurresan bai if: theory2, shape eta esfera
  - theory1 2 theory tamaina
  - theory0 2 theory tamaina
Cost model: exception kostatu 2.
Data:
  - item: i0, gorri, kubo eta no
  - item: i1, gorri, esfera eta no
  - item: i5, gorri, pinaburu eta bai
  - item: i7, berde, kubo eta no
  - item: i4, urdin, pinaburu eta no
  - item: i6, gorri, pinaburu eta no
  - item: i3, berde, esfera eta no
  - item: i2, gorri, pinaburu eta no
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - predict ᎥᎥ if: theory3, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory2, shape ᎠᎴ sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
  - item: i1, ᎩᎦᎨ, sphere ᎠᎴ ᏝᏃ
  - item: i5, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
  - item: i7, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i4, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᏝᏃ
  - item: i6, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i3, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᏝᏃ
  - item: i2, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory2 theory chhika 2
  - theory3 theory chhika 4
  - predict arí if: theory3, llimphi waylla
  - predict arí if: theory0, llimphi puka
  - predict arí if: theory1, llimphi anqas
  - predict arí if: theory3, shape sphere
  - predict arí if: theory2, shape sphere
  - theory1 theory chhika 2
  - theory0 theory chhika 2
Cost model: exception cost 2.
Data:
  - item: i0, puka, cube mánan
  - item: i1, puka, sphere mánan
  - item: i5, puka, cone arí
  - item: i7, waylla, cube mánan
  - item: i4, anqas, cone mánan
  - item: i6, puka, cone mánan
  - item: i3, waylla, sphere mánan
  - item: i2, puka, cone mánan
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 тиориэ size
  - theory3 4 тиориэ size
  - predict yes if: theory3, color ыкӏи уцышъо
  - predict yes if: theory0, color ыкӏи плъыжьы
  - predict yes if: theory1, color ыкӏи blue
  - predict yes if: theory3, shape ыкӏи sphere
  - predict yes if: theory2, shape ыкӏи sphere
  - theory1 2 тиориэ size
  - theory0 2 тиориэ size
Cost model: exception cost 2.
Data:
  - item: i0, плъыжьы, cube ыкӏи no
  - item: i1, плъыжьы, sphere ыкӏи no
  - item: i5, плъыжьы, cone ыкӏи yes
  - item: i7, уцышъо, cube ыкӏи no
  - item: i4, blue, cone ыкӏи no
  - item: i6, плъыжьы, cone ыкӏи no
  - item: i3, уцышъо, sphere ыкӏи no
  - item: i2, плъыжьы, cone ыкӏи no
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, xim thiab ntsuab
  - predict yes if: theory0, xim thiab liab
  - predict yes if: theory1, xim thiab xiav
  - predict yes if: theory3, shape thiab sphere
  - predict yes if: theory2, shape thiab sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception nqi 2.
Data:
  - item: i0, liab, cube thiab no
  - item: i1, liab, sphere thiab no
  - item: i5, liab, cone thiab yes
  - item: i7, ntsuab, cube thiab no
  - item: i4, xiav, cone thiab no
  - item: i6, liab, cone thiab no
  - item: i3, ntsuab, sphere thiab no
  - item: i2, liab, cone thiab no
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict ngai if: theory3, color green
  - predict ngai if: theory0, color red
  - predict ngai if: theory1, color blue
  - predict ngai if: theory3, shape sphere
  - predict ngai if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube woi
  - item: i1, red, sphere woi
  - item: i5, red, cone ngai
  - item: i7, green, cube woi
  - item: i4, blue, cone woi
  - item: i6, red, cone woi
  - item: i3, green, sphere woi
  - item: i2, red, cone woi
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict i̠i if: theory3, color micha' green
  - predict i̠i if: theory0, color micha' homa
  - predict i̠i if: theory1, color micha' blue
  - predict i̠i if: theory3, shape micha' sphere
  - predict i̠i if: theory2, shape micha' sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, homa, cube micha' no
  - item: i1, homa, sphere micha' no
  - item: i5, homa, cone micha' i̠i
  - item: i7, green, cube micha' no
  - item: i4, blue, cone micha' no
  - item: i6, homa, cone micha' no
  - item: i3, green, sphere micha' no
  - item: i2, homa, cone micha' no
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, color カ green
  - predict yes if: theory0, color カ フレ
  - predict yes if: theory1, color カ blue
  - predict yes if: theory3, shape カ sphere
  - predict yes if: theory2, shape カ sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, フレ, cube カ ソモ
  - item: i1, フレ, sphere カ ソモ
  - item: i5, フレ, cone カ yes
  - item: i7, green, cube カ ソモ
  - item: i4, blue, cone カ ソモ
  - item: i6, フレ, cone カ ソモ
  - item: i3, green, sphere カ ソモ
  - item: i2, フレ, cone カ ソモ
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes эвыр: theory3, пэран ынкъам green
  - predict yes эвыр: theory0, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory1, пэран ынкъам ныԓиԓиԓӄин
  - predict yes эвыр: theory3, shape ынкъам sphere
  - predict yes эвыр: theory2, shape ынкъам sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, чеԓгыԓьын, cube ынкъам no
  - item: i1, чеԓгыԓьын, sphere ынкъам no
  - item: i5, чеԓгыԓьын, cone ынкъам yes
  - item: i7, green, cube ынкъам no
  - item: i4, ныԓиԓиԓӄин, cone ынкъам no
  - item: i6, чеԓгыԓьын, cone ынкъам no
  - item: i3, green, sphere ынкъам no
  - item: i2, чеԓгыԓьын, cone ынкъам no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes wa: theory3, color yéetel yaʼax
  - predict yes wa: theory0, color yéetel chak
  - predict yes wa: theory1, color yéetel blue
  - predict yes wa: theory3, shape yéetel sphere
  - predict yes wa: theory2, shape yéetel sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, chak, cube yéetel no
  - item: i1, chak, sphere yéetel no
  - item: i5, chak, cone yéetel yes
  - item: i7, yaʼax, cube yéetel no
  - item: i4, blue, cone yéetel no
  - item: i6, chak, cone yéetel no
  - item: i3, yaʼax, sphere yéetel no
  - item: i2, chak, cone yéetel no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict hą if: theory3, color green
  - predict hą if: theory0, color red
  - predict hą if: theory1, color blue
  - predict hą if: theory3, shape sphere
  - predict hą if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone hą
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, red, cone no
  - item: i3, green, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, color na green
  - predict yes if: theory0, color na red
  - predict yes if: theory1, color na blue
  - predict yes if: theory3, shape na sphere
  - predict yes if: theory2, shape na sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, red, cube na no
  - item: i1, red, sphere na no
  - item: i5, red, cone na yes
  - item: i7, green, cube na no
  - item: i4, blue, cone na no
  - item: i6, red, cone na no
  - item: i3, green, sphere na no
  - item: i2, red, cone na no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict heẽ if: theory3, color green
  - predict heẽ if: theory0, color pytã
  - predict heẽ if: theory1, color blue
  - predict heẽ if: theory3, shape sphere
  - predict heẽ if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, pytã, cube nahániri
  - item: i1, pytã, sphere nahániri
  - item: i5, pytã, cone heẽ
  - item: i7, green, cube nahániri
  - item: i4, blue, cone nahániri
  - item: i6, pytã, cone nahániri
  - item: i3, green, sphere nahániri
  - item: i2, pytã, cone nahániri
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict jisa if: theory3, sami ch’uxña
  - predict jisa if: theory0, sami chupika
  - predict jisa if: theory1, sami larama
  - predict jisa if: theory3, shape sphere
  - predict jisa if: theory2, shape sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, chupika, cube no
  - item: i1, chupika, sphere no
  - item: i5, chupika, cone jisa
  - item: i7, ch’uxña, cube no
  - item: i4, larama, cone no
  - item: i6, chupika, cone no
  - item: i3, ch’uxña, sphere no
  - item: i2, chupika, cone no
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory ʻǁnàhã 2
  - theory3 theory ʻǁnàhã 4
  - predict ʻée if: theory3, color ǂʻá green
  - predict ʻée if: theory0, color ǂʻá red
  - predict ʻée if: theory1, color ǂʻá blue
  - predict ʻée if: theory3, shape ǂʻá sphere
  - predict ʻée if: theory2, shape ǂʻá sphere
  - theory1 theory ʻǁnàhã 2
  - theory0 theory ʻǁnàhã 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube ǂʻá ʻĩĩ
  - item: i1, red, sphere ǂʻá ʻĩĩ
  - item: i5, red, cone ǂʻá ʻée
  - item: i7, green, cube ǂʻá ʻĩĩ
  - item: i4, blue, cone ǂʻá ʻĩĩ
  - item: i6, red, cone ǂʻá ʻĩĩ
  - item: i3, green, sphere ǂʻá ʻĩĩ
  - item: i2, red, cone ǂʻá ʻĩĩ
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, вал green
  - predict yes if: theory0, вал red
  - predict yes if: theory1, вал blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone yes
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, red, cone no
  - item: i3, green, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, color green
  - predict yes if: theory0, color red
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone yes
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, red, cone no
  - item: i3, green, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, color green
  - predict yes if: theory0, color red
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone yes
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, red, cone no
  - item: i3, green, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict ee if: theory3, color green
  - predict ee if: theory0, color red
  - predict ee if: theory1, color blue
  - predict ee if: theory3, shape sphere
  - predict ee if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone ee
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, red, cone no
  - item: i3, green, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, color green
  - predict yes if: theory0, color red
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube inte
  - item: i1, red, sphere inte
  - item: i5, red, cone yes
  - item: i7, green, cube inte
  - item: i4, blue, cone inte
  - item: i6, red, cone inte
  - item: i3, green, sphere inte
  - item: i2, red, cone inte
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - theory2 théorie taille 2
  - theory3 théorie taille 4
  - prédire oui si: theory3, couleur et vert
  - prédire oui si: theory0, couleur et rouge
  - prédire oui si: theory1, couleur et bleu
  - prédire oui si: theory3, forme et sphère
  - prédire oui si: theory2, forme et sphère
  - theory1 théorie taille 2
  - theory0 théorie taille 2
Coûts : exception coûter 2.
Les données sont:
  - article: i0, rouge, cube et no
  - article: i1, rouge, sphère et no
  - article: i5, rouge, cône et oui
  - article: i7, vert, cube et no
  - article: i4, bleu, cône et no
  - article: i6, rouge, cône et no
  - article: i3, vert, sphère et no
  - article: i2, rouge, cône et no
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - theory2 teorio grandeco 2
  - theory3 teorio grandeco 4
  - predict jes se: theory3, koloro kaj verda
  - predict jes se: theory0, koloro kaj ruĝa
  - predict jes se: theory1, koloro kaj blua
  - predict jes se: theory3, formi kaj sfero
  - predict jes se: theory2, formi kaj sfero
  - theory1 teorio grandeco 2
  - theory0 teorio grandeco 2
Kosto pozistino: escepto kosti 2.
Datumo:
  - ero: i0, ruĝa, kubo kaj no
  - ero: i1, ruĝa, sfero kaj no
  - ero: i5, ruĝa, strobilo kaj jes
  - ero: i7, verda, kubo kaj no
  - ero: i4, blua, strobilo kaj no
  - ero: i6, ruĝa, strobilo kaj no
  - ero: i3, verda, sfero kaj no
  - ero: i2, ruĝa, strobilo kaj no
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - theory2 elmélet méret 2
  - theory3 elmélet méret 4
  - megjósol igen ha: theory3, szín és zöld
  - megjósol igen ha: theory0, szín és piros
  - megjósol igen ha: theory1, szín és kék
  - megjósol igen ha: theory3, alak és gömb
  - megjósol igen ha: theory2, alak és gömb
  - theory1 elmélet méret 2
  - theory0 elmélet méret 2
Költség manöken: kivétel kerül 2.
Adat:
  - cikk: i0, piros, kocka és no
  - cikk: i1, piros, gömb és no
  - cikk: i5, piros, toboz és igen
  - cikk: i7, zöld, kocka és no
  - cikk: i4, kék, toboz és no
  - cikk: i6, piros, toboz és no
  - cikk: i3, zöld, gömb és no
  - cikk: i2, piros, toboz és no
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - theory2theory size2
  - theory3theory size4
  - predict係要是:theory3, 色及綠色嘅
  - predict係要是:theory0, 色及紅
  - predict係要是:theory1, 色及藍色嘅
  - predict係要是:theory3, 形狀及sphere
  - predict係要是:theory2, 形狀及sphere
  - theory1theory size2
  - theory0theory size2
cost model:exception cost:2.
數據:
  - item:i0, 紅, cube及no
  - item:i1, 紅, sphere及no
  - item:i5, 紅, cone及係
  - item:i7, 綠色嘅, cube及no
  - item:i4, 藍色嘅, cone及no
  - item:i6, 紅, cone及no
  - item:i3, 綠色嘅, sphere及no
  - item:i2, 紅, cone及no
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, color とぅ green
  - predict yes if: theory0, color とぅ red
  - predict yes if: theory1, color とぅ blue
  - predict yes if: theory3, shape とぅ sphere
  - predict yes if: theory2, shape とぅ sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube とぅ no
  - item: i1, red, sphere とぅ no
  - item: i5, red, cone とぅ yes
  - item: i7, green, cube とぅ no
  - item: i4, blue, cone とぅ no
  - item: i6, red, cone とぅ no
  - item: i3, green, sphere とぅ no
  - item: i2, red, cone とぅ no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - theory2 теория мөлшер 2
  - theory3 теория мөлшер 4
  - predict иә егер: theory3, түс және жасыл
  - predict иә егер: theory0, түс және қызыл
  - predict иә егер: theory1, түс және көк
  - predict иә егер: theory3, нысан және сфера
  - predict иә егер: theory2, нысан және сфера
  - theory1 теория мөлшер 2
  - theory0 теория мөлшер 2
Тұру үлгі: ерекшелік тұру 2.
Деректер:
  - зат: i0, қызыл, куб және жоқ
  - зат: i1, қызыл, сфера және жоқ
  - зат: i5, қызыл, бүршік және иә
  - зат: i7, жасыл, куб және жоқ
  - зат: i4, көк, бүршік және жоқ
  - зат: i6, қызыл, бүршік және жоқ
  - зат: i3, жасыл, сфера және жоқ
  - зат: i2, қызыл, бүршік және жоқ
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - theory2 תּוֹרָה גֹּדֶל 2
  - theory3 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory3, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory0, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory1, צֶבַע וְ־ כָּחוֹל
  - predict כֵּן אִם: theory3, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory2, צוּרָה וְ־ כַּדּוּר
  - theory1 תּוֹרָה גֹּדֶל 2
  - theory0 תּוֹרָה גֹּדֶל 2
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 2.
נְתוּנִים:
  - אבזר: i0, אָדוֹם, קובייה וְ־ no
  - אבזר: i1, אָדוֹם, כַּדּוּר וְ־ no
  - אבזר: i5, אָדוֹם, אצטרובל וְ־ כֵּן
  - אבזר: i7, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i4, כָּחוֹל, אצטרובל וְ־ no
  - אבזר: i6, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i3, ירוק מקנאה, כַּדּוּר וְ־ no
  - אבזר: i2, אָדוֹם, אצטרובל וְ־ no
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict იახ if: theory3, color დო green
  - predict იახ if: theory0, color დო red
  - predict იახ if: theory1, color დო blue
  - predict იახ if: theory3, shape დო sphere
  - predict იახ if: theory2, shape დო sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube დო no
  - item: i1, red, sphere დო no
  - item: i5, red, cone დო იახ
  - item: i7, green, cube დო no
  - item: i4, blue, cone დო no
  - item: i6, red, cone დო no
  - item: i3, green, sphere დო no
  - item: i2, red, cone დო no
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - theory2 teori ukuran 2
  - theory3 teori ukuran 4
  - predict ya jika: theory3, warna dan hijau
  - predict ya jika: theory0, warna dan merah
  - predict ya jika: theory1, warna dan biru
  - predict ya jika: theory3, bentuk dan bola
  - predict ya jika: theory2, bentuk dan bola
  - theory1 teori ukuran 2
  - theory0 teori ukuran 2
Cost model: pengecualian menghabiskan biaya 2.
Data:
  - benda: i0, merah, kubus dan no
  - benda: i1, merah, bola dan no
  - benda: i5, merah, kerucut dan ya
  - benda: i7, hijau, kubus dan no
  - benda: i4, biru, kerucut dan no
  - benda: i6, merah, kerucut dan no
  - benda: i3, hijau, bola dan no
  - benda: i2, merah, kerucut dan no
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - theory2ទ្រឹស្ដីទំហំ2
  - theory3ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងខៀវ
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory2, គ្រោងនឹងគោល
  - theory1ទ្រឹស្ដីទំហំ2
  - theory0ទ្រឹស្ដីទំហំ2
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:2.
ទិន្នន័យ:
  - item:i0, ក្រហម, គូបនឹងទេ
  - item:i1, ក្រហម, គោលនឹងទេ
  - item:i5, ក្រហម, កោណនឹងច៎ា
  - item:i7, បៃតង, គូបនឹងទេ
  - item:i4, ខៀវ, កោណនឹងទេ
  - item:i6, ក្រហម, កោណនឹងទេ
  - item:i3, បៃតង, គោលនឹងទេ
  - item:i2, ក្រហម, កោណនឹងទេ
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - theory2ທິດສະດີຂະຫນາດ2
  - theory3ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຟ້າ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ຮູບຮ່າງແລະຄະດີ
  - theory1ທິດສະດີຂະຫນາດ2
  - theory0ທິດສະດີຂະຫນາດ2
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:2.
ຂໍ້ມູນ:
  - item:i0, ແດງ, ລູກບາດແລະno
  - item:i1, ແດງ, ຄະດີແລະno
  - item:i5, ແດງ, coneແລະແມ່ນ
  - item:i7, ຂຽວ, ລູກບາດແລະno
  - item:i4, ຟ້າ, coneແລະno
  - item:i6, ແດງ, coneແລະno
  - item:i3, ຂຽວ, ຄະດີແລະno
  - item:i2, ແດງ, coneແລະno
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict అవును అయితే: theory3, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory0, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory1, రంగు మరియు నీలము
  - predict అవును అయితే: theory3, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory2, ఆకారం మరియు గోళము
  - theory1 2 theory size
  - theory0 2 theory size
cost model: exception cost 2.
data:
  - item: i0, ఎర్ర, cube మరియు no
  - item: i1, ఎర్ర, గోళము మరియు no
  - item: i5, ఎర్ర, cone మరియు అవును
  - item: i7, పచ్చని, cube మరియు no
  - item: i4, నీలము, cone మరియు no
  - item: i6, ఎర్ర, cone మరియు no
  - item: i3, పచ్చని, గోళము మరియు no
  - item: i2, ఎర్ర, cone మరియు no
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict hẹn if: theory3, color green
  - predict hẹn if: theory0, color pupa
  - predict hẹn if: theory1, color dúdú
  - predict hẹn if: theory3, shape sphere
  - predict hẹn if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, pupa, cube rárá
  - item: i1, pupa, sphere rárá
  - item: i5, pupa, cone bẹ́ẹ̀ ni
  - item: i7, green, cube rárá
  - item: i4, dúdú, cone rárá
  - item: i6, pupa, cone rárá
  - item: i3, green, sphere rárá
  - item: i2, pupa, cone rárá
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict au if: theory3, color green
  - predict au if: theory0, color łichíí
  - predict au if: theory1, color blue
  - predict au if: theory3, shape sphere
  - predict au if: theory2, shape sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, łichíí, cube no
  - item: i1, łichíí, sphere no
  - item: i5, łichíí, cone au
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, łichíí, cone no
  - item: i3, green, sphere no
  - item: i2, łichíí, cone no
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, color green
  - predict yes if: theory0, color red
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
cost model: exception cost 2.
data:
  - item: i0, red, cube ᐋᒃᑲ
  - item: i1, red, sphere ᐋᒃᑲ
  - item: i5, red, cone yes
  - item: i7, green, cube ᐋᒃᑲ
  - item: i4, blue, cone ᐋᒃᑲ
  - item: i6, red, cone ᐋᒃᑲ
  - item: i3, green, sphere ᐋᒃᑲ
  - item: i2, red, cone ᐋᒃᑲ
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, color grun
  - predict yes if: theory0, color red
  - predict yes if: theory1, color blaw
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone yes
  - item: i7, grun, cube no
  - item: i4, blaw, cone no
  - item: i6, red, cone no
  - item: i3, grun, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory3 theory size 4
  - predict yes if: theory3, color green
  - predict yes if: theory0, color red
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 theory size 2
  - theory0 theory size 2
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone yes
  - item: i7, green, cube no
  - item: i4, blue, cone no
  - item: i6, red, cone no
  - item: i3, green, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - theory3 4 theory size
  - predict yes if: theory3, ранг къацу
  - predict yes if: theory0, ранг red
  - predict yes if: theory1, ранг blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - theory1 2 theory size
  - theory0 2 theory size
Cost model: exception cost 2.
Data:
  - item: i0, red, cube no
  - item: i1, red, sphere no
  - item: i5, red, cone yes
  - item: i7, къацу, cube no
  - item: i4, blue, cone no
  - item: i6, red, cone no
  - item: i3, къацу, sphere no
  - item: i2, red, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - predicts yes if: theory1, colour and red
  - theory1 theory size 4
  - predicts yes if: theory0, shape and cone
  - predicts yes if: theory1, colour and green
  - theory3 theory size 2
  - predicts yes if: theory2, shape and cone
  - predicts yes if: theory3, shape and cube
  - theory0 theory size 4
  - predicts yes if: theory2, shape and cube
  - theory2 theory size 4
  - predicts yes if: theory0, shape and cone
Costs: exception cost 4.
The data are:
  - item: i0, red, cone and yes
  - item: i5, green, cube and no
  - item: i4, green, cube and yes
  - item: i6, blue, sphere and yes
  - item: i7, green, cube and yes
  - item: i1, green, cube and no
  - item: i3, blue, cone and no
  - item: i2, blue, cone and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - predice sí si: theory1, es de color y rojo
  - theory1 el tamaño de la teoría 4
  - predice sí si: theory0, forma y cono
  - predice sí si: theory1, es de color y verde
  - theory3 el tamaño de la teoría 2
  - predice sí si: theory2, forma y cono
  - predice sí si: theory3, forma y cubo
  - theory0 el tamaño de la teoría 4
  - predice sí si: theory2, forma y cubo
  - theory2 el tamaño de la teoría 4
  - predice sí si: theory0, forma y cono
Costes: exception el coste: 4.
Los datos son:
  - el elemento: i0, rojo, cono y sí
  - el elemento: i5, verde, cubo y no
  - el elemento: i4, verde, cubo y sí
  - el elemento: i6, azul, esfera y sí
  - el elemento: i7, verde, cubo y sí
  - el elemento: i1, verde, cubo y no
  - el elemento: i3, azul, cono y no
  - el elemento: i2, azul, cono y no
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - 预测是若：theory1、颜色和红色的
  - theory1的理论规模是4
  - 预测是若：theory0、形状和圆锥
  - 预测是若：theory1、颜色和绿色的
  - theory3的理论规模是2
  - 预测是若：theory2、形状和圆锥
  - 预测是若：theory3、形状和立方体
  - theory0的理论规模是4
  - 预测是若：theory2、形状和立方体
  - theory2的理论规模是4
  - 预测是若：theory0、形状和圆锥
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i0、红色的、圆锥和是
  - 的物品是：i5、绿色的、立方体和否
  - 的物品是：i4、绿色的、立方体和是
  - 的物品是：i6、蓝色的、球体和是
  - 的物品是：i7、绿色的、立方体和是
  - 的物品是：i1、绿色的、立方体和否
  - 的物品是：i3、蓝色的、圆锥和否
  - 的物品是：i2、蓝色的、圆锥和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - öngörür evet eğer: theory1, renk ve kırmızı
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory0, şekil ve koni
  - öngörür evet eğer: theory1, renk ve yeşil
  - theory3 2 kuram boyut
  - öngörür evet eğer: theory2, şekil ve koni
  - öngörür evet eğer: theory3, şekil ve küp
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory2, şekil ve küp
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory0, şekil ve koni
Cost model: exception maliyet: 4.
Veriler:
  - item: i0, kırmızı, koni ve evet
  - item: i5, yeşil, küp ve hayır
  - item: i4, yeşil, küp ve evet
  - item: i6, mavi, küre ve evet
  - item: i7, yeşil, küp ve evet
  - item: i1, yeşil, küp ve hayır
  - item: i3, mavi, koni ve hayır
  - item: i2, mavi, koni ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - predict ndiyo kama: theory1, rangi na ekundu
  - theory1 theory size 4
  - predict ndiyo kama: theory0, umbo na koni
  - predict ndiyo kama: theory1, rangi na kijani
  - theory3 theory size 2
  - predict ndiyo kama: theory2, umbo na koni
  - predict ndiyo kama: theory3, umbo na mchemraba
  - theory0 theory size 4
  - predict ndiyo kama: theory2, umbo na mchemraba
  - theory2 theory size 4
  - predict ndiyo kama: theory0, umbo na koni
Cost model: exception gharimu: 4.
Data:
  - item: i0, ekundu, koni na ndiyo
  - item: i5, kijani, mchemraba na hapana
  - item: i4, kijani, mchemraba na ndiyo
  - item: i6, buluu, tufe na ndiyo
  - item: i7, kijani, mchemraba na ndiyo
  - item: i1, kijani, mchemraba na hapana
  - item: i3, buluu, koni na hapana
  - item: i2, buluu, koni na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - predicts yes if: theory1, colour and red
  - theory1 theory size 4
  - predicts yes if: theory0, shape and cone
  - predicts yes if: theory1, colour and green
  - theory3 theory size 2
  - predicts yes if: theory2, shape and cone
  - predicts yes if: theory3, shape and cube
  - theory0 theory size 4
  - predicts yes if: theory2, shape and cube
  - theory2 theory size 4
  - predicts yes if: theory0, shape and cone
Costs: exception cost 4.
The data are:
  - item: i0, red, cone and yes
  - item: i5, green, cube and no
  - item: i4, green, cube and yes
  - item: i6, blue, sphere and yes
  - item: i7, green, cube and yes
  - item: i1, green, cube and no
  - item: i3, blue, cone and no
  - item: i2, blue, cone and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(predicts_yes_if theory1 color red) (theory_size theory1 4) (predicts_yes_if theory0 shape cone) (predicts_yes_if theory1 color green) (theory_size theory3 2) (predicts_yes_if theory2 shape cone) (predicts_yes_if theory3 shape cube) (theory_size theory0 4) (predicts_yes_if theory2 shape cube) (theory_size theory2 4) (predicts_yes_if theory0 shape cone)], cost_model: [(exception_cost 4)], data: [(item i0 red cone yes) (item i5 green cube no) (item i4 green cube yes) (item i6 blue sphere yes) (item i7 green cube yes) (item i1 green cube no) (item i3 blue cone no) (item i2 blue cone no)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - vorhersagen ja wenn: theory1, Farbe und rot
  - theory1 Theorie Größe 4
  - vorhersagen ja wenn: theory0, Form und Kegel
  - vorhersagen ja wenn: theory1, Farbe und grün
  - theory3 Theorie Größe 2
  - vorhersagen ja wenn: theory2, Form und Kegel
  - vorhersagen ja wenn: theory3, Form und Kubus
  - theory0 Theorie Größe 4
  - vorhersagen ja wenn: theory2, Form und Kubus
  - theory2 Theorie Größe 4
  - vorhersagen ja wenn: theory0, Form und Kegel
Kosten: Ausnahme kosten 4.
Die Daten sind:
  - Ding: i0, rot, Kegel und ja
  - Ding: i5, grün, Kubus und nein
  - Ding: i4, grün, Kubus und ja
  - Ding: i6, blau, Kugel und ja
  - Ding: i7, grün, Kubus und ja
  - Ding: i1, grün, Kubus und nein
  - Ding: i3, blau, Kegel und nein
  - Ding: i2, blau, Kegel und nein
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - predvidjeti да ȁko: theory1, бо̀ја и цр̀вен
  - theory1 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory0, о̏блӣк и stožac
  - predvidjeti да ȁko: theory1, бо̀ја и зѐлен
  - theory3 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory2, о̏блӣк и stožac
  - predvidjeti да ȁko: theory3, о̏блӣк и коцка
  - theory0 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory2, о̏блӣк и коцка
  - theory2 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory0, о̏блӣк и stožac
Trošak мо̀дел: изузе́так ко̏штати 4.
Податци:
  - item: i0, цр̀вен, stožac и да
  - item: i5, зѐлен, коцка и no
  - item: i4, зѐлен, коцка и да
  - item: i6, пла̑в, сфе́ра и да
  - item: i7, зѐлен, коцка и да
  - item: i1, зѐлен, коцка и no
  - item: i3, пла̑в, stožac и no
  - item: i2, пла̑в, stožac и no
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - ennakoida kyllä jos: theory1, väri ja punainen
  - theory1 teoria koko 4
  - ennakoida kyllä jos: theory0, muoto ja kartio
  - ennakoida kyllä jos: theory1, väri ja vihreä
  - theory3 teoria koko 2
  - ennakoida kyllä jos: theory2, muoto ja kartio
  - ennakoida kyllä jos: theory3, muoto ja kuutio
  - theory0 teoria koko 4
  - ennakoida kyllä jos: theory2, muoto ja kuutio
  - theory2 teoria koko 4
  - ennakoida kyllä jos: theory0, muoto ja kartio
Kustannus malli: poikkeus maksaa 4.
Tieto:
  - esine: i0, punainen, kartio ja kyllä
  - esine: i5, vihreä, kuutio ja ei
  - esine: i4, vihreä, kuutio ja kyllä
  - esine: i6, sininen, pallo ja kyllä
  - esine: i7, vihreä, kuutio ja kyllä
  - esine: i1, vihreä, kuutio ja ei
  - esine: i3, sininen, kartio ja ei
  - esine: i2, sininen, kartio ja ei
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - 预测是若：theory1、颜色和红色的
  - theory1的理论规模是4
  - 预测是若：theory0、形状和圆锥
  - 预测是若：theory1、颜色和绿色的
  - theory3的理论规模是2
  - 预测是若：theory2、形状和圆锥
  - 预测是若：theory3、形状和立方体
  - theory0的理论规模是4
  - 预测是若：theory2、形状和立方体
  - theory2的理论规模是4
  - 预测是若：theory0、形状和圆锥
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i0、红色的、圆锥和是
  - 的物品是：i5、绿色的、立方体和否
  - 的物品是：i4、绿色的、立方体和是
  - 的物品是：i6、蓝色的、球体和是
  - 的物品是：i7、绿色的、立方体和是
  - 的物品是：i1、绿色的、立方体和否
  - 的物品是：i3、蓝色的、圆锥和否
  - 的物品是：i2、蓝色的、圆锥和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - 予言するはいもし...:theory1, 色と赤い
  - theory14理論大きさ
  - 予言するはいもし...:theory0, 形と松毬
  - 予言するはいもし...:theory1, 色と緑の
  - theory32理論大きさ
  - 予言するはいもし...:theory2, 形と松毬
  - 予言するはいもし...:theory3, 形と立方体
  - theory04理論大きさ
  - 予言するはいもし...:theory2, 形と立方体
  - theory24理論大きさ
  - 予言するはいもし...:theory0, 形と松毬
費用モデル:例外費やす:4.
資料:
  - 項目:i0, 赤い, 松毬とはい
  - 項目:i5, 緑の, 立方体といいえ
  - 項目:i4, 緑の, 立方体とはい
  - 項目:i6, 青い, 球とはい
  - 項目:i7, 緑の, 立方体とはい
  - 項目:i1, 緑の, 立方体といいえ
  - 項目:i3, 青い, 松毬といいえ
  - 項目:i2, 青い, 松毬といいえ
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - öngörür evet eğer: theory1, renk ve kırmızı
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory0, şekil ve koni
  - öngörür evet eğer: theory1, renk ve yeşil
  - theory3 2 kuram boyut
  - öngörür evet eğer: theory2, şekil ve koni
  - öngörür evet eğer: theory3, şekil ve küp
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory2, şekil ve küp
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory0, şekil ve koni
Cost model: exception maliyet: 4.
Veriler:
  - item: i0, kırmızı, koni ve evet
  - item: i5, yeşil, küp ve hayır
  - item: i4, yeşil, küp ve evet
  - item: i6, mavi, küre ve evet
  - item: i7, yeşil, küp ve evet
  - item: i1, yeşil, küp ve hayır
  - item: i3, mavi, koni ve hayır
  - item: i2, mavi, koni ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَحْمَر
  - نَظَرِيَّة مَقَاس theory1 4
  - تَنَبَّأَ نَعَم إِنْ: theory0، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory3 2
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كَعْبَة
  - نَظَرِيَّة مَقَاس theory0 4
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ كَعْبَة
  - نَظَرِيَّة مَقَاس theory2 4
  - تَنَبَّأَ نَعَم إِنْ: theory0، شَكْل وَـ مَخْرُوط
cost model: اِسْتِثْنَاء cost 4.
بَيَانَات:
  - بَنْد: i0، أَحْمَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i5، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i4، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i6، أَزْرَق، كُرَة وَـ نَعَم
  - بَنْد: i7، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i1، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i3، أَزْرَق، مَخْرُوط وَـ no
  - بَنْد: i2، أَزْرَق، مَخْرُوط وَـ no
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - predict დიახ თუ: theory1, ფერი და წითელი
  - theory1 4 თეორია ზომა
  - predict დიახ თუ: theory0, ფორმა და გირჩა
  - predict დიახ თუ: theory1, ფერი და მწვანე
  - theory3 2 თეორია ზომა
  - predict დიახ თუ: theory2, ფორმა და გირჩა
  - predict დიახ თუ: theory3, ფორმა და კუბი
  - theory0 4 თეორია ზომა
  - predict დიახ თუ: theory2, ფორმა და კუბი
  - theory2 4 თეორია ზომა
  - predict დიახ თუ: theory0, ფორმა და გირჩა
Ფასი მოდელი: გამონაკლისი ღირს 4.
Მონაცემები:
  - item: i0, წითელი, გირჩა და დიახ
  - item: i5, მწვანე, კუბი და no
  - item: i4, მწვანე, კუბი და დიახ
  - item: i6, ლურჯი, სფერო და დიახ
  - item: i7, მწვანე, კუბი და დიახ
  - item: i1, მწვანე, კუბი და no
  - item: i3, ლურჯი, გირჩა და no
  - item: i2, ლურჯი, გირჩა და no
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - matakite āe mēnā: theory1, kano me red
  - ariā size theory1 4
  - matakite āe mēnā: theory0, āhuahanga me koeko
  - matakite āe mēnā: theory1, kano me kākāriki
  - ariā size theory3 2
  - matakite āe mēnā: theory2, āhuahanga me koeko
  - matakite āe mēnā: theory3, āhuahanga me mataono
  - ariā size theory0 4
  - matakite āe mēnā: theory2, āhuahanga me mataono
  - ariā size theory2 4
  - matakite āe mēnā: theory0, āhuahanga me koeko
Cost model: hāungatanga cost 4.
Hōtuku:
  - tūemi: i0, red, koeko me āe
  - tūemi: i5, kākāriki, mataono me kāo
  - tūemi: i4, kākāriki, mataono me āe
  - tūemi: i6, kahurangi, poi me āe
  - tūemi: i7, kākāriki, mataono me āe
  - tūemi: i1, kākāriki, mataono me kāo
  - tūemi: i3, kahurangi, koeko me kāo
  - tūemi: i2, kahurangi, koeko me kāo
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - dự báo vâng nếu: theory1, màu và đỏ
  - theory1 lý thuyết kích thước 4
  - dự báo vâng nếu: theory0, hình dáng và nón
  - dự báo vâng nếu: theory1, màu và green
  - theory3 lý thuyết kích thước 2
  - dự báo vâng nếu: theory2, hình dáng và nón
  - dự báo vâng nếu: theory3, hình dáng và lập phương
  - theory0 lý thuyết kích thước 4
  - dự báo vâng nếu: theory2, hình dáng và lập phương
  - theory2 lý thuyết kích thước 4
  - dự báo vâng nếu: theory0, hình dáng và nón
Cost model: lệ ngoại cost 4.
Dữ liệu:
  - vật phẩm: i0, đỏ, nón và vâng
  - vật phẩm: i5, green, lập phương và no
  - vật phẩm: i4, green, lập phương và vâng
  - vật phẩm: i6, blue, mặt cầu và vâng
  - vật phẩm: i7, green, lập phương và vâng
  - vật phẩm: i1, green, lập phương và no
  - vật phẩm: i3, blue, nón và no
  - vật phẩm: i2, blue, nón và no
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - ทำนายใช่ถ้า หาก:theory1, สีและแดง
  - theory1ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory0, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory1, สีและเขียว
  - theory3ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและลูกบาศก์
  - theory0ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและลูกบาศก์
  - theory2ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory0, รูปร่างและกรวย
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:4.
ข้อมูล:
  - item:i0, แดง, กรวยและใช่
  - item:i5, เขียว, ลูกบาศก์และไฮ้
  - item:i4, เขียว, ลูกบาศก์และใช่
  - item:i6, น้ำเงิน, ทรงกลมและใช่
  - item:i7, เขียว, ลูกบาศก์และใช่
  - item:i1, เขียว, ลูกบาศก์และไฮ้
  - item:i3, น้ำเงิน, กรวยและไฮ้
  - item:i2, น้ำเงิน, กรวยและไฮ้
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் சிவப்பு
  - theory1 4 theory size
  - கணி ஆம் என்றால்: theory0, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் பச்சை
  - theory3 2 theory size
  - கணி ஆம் என்றால்: theory2, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory3, shape மற்றும் cube
  - theory0 4 theory size
  - கணி ஆம் என்றால்: theory2, shape மற்றும் cube
  - theory2 4 theory size
  - கணி ஆம் என்றால்: theory0, shape மற்றும் cone
cost model: exception செலவு 4.
தரவுகள்:
  - item: i0, சிவப்பு, cone மற்றும் ஆம்
  - item: i5, பச்சை, cube மற்றும் no
  - item: i4, பச்சை, cube மற்றும் ஆம்
  - item: i6, நீலம், sphere மற்றும் ஆம்
  - item: i7, பச்சை, cube மற்றும் ஆம்
  - item: i1, பச்சை, cube மற்றும் no
  - item: i3, நீலம், cone மற்றும் no
  - item: i2, நீலம், cone மற்றும் no
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - predict ndiyo kama: theory1, rangi na ekundu
  - theory1 theory size 4
  - predict ndiyo kama: theory0, umbo na koni
  - predict ndiyo kama: theory1, rangi na kijani
  - theory3 theory size 2
  - predict ndiyo kama: theory2, umbo na koni
  - predict ndiyo kama: theory3, umbo na mchemraba
  - theory0 theory size 4
  - predict ndiyo kama: theory2, umbo na mchemraba
  - theory2 theory size 4
  - predict ndiyo kama: theory0, umbo na koni
Cost model: exception gharimu: 4.
Data:
  - item: i0, ekundu, koni na ndiyo
  - item: i5, kijani, mchemraba na hapana
  - item: i4, kijani, mchemraba na ndiyo
  - item: i6, buluu, tufe na ndiyo
  - item: i7, kijani, mchemraba na ndiyo
  - item: i1, kijani, mchemraba na hapana
  - item: i3, buluu, koni na hapana
  - item: i2, buluu, koni na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó łichííʼ
  - theory1 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory3 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó cube
  - theory0 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó cube
  - theory2 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, shape dóó cone
Cost model: exception bą́ą́h ílį́ 4.
Data:
  - item: i0, łichííʼ, cone dóó aooʼ
  - item: i5, dinootłʼizh, cube dóó no
  - item: i4, dinootłʼizh, cube dóó aooʼ
  - item: i6, dootłʼizh, sphere dóó aooʼ
  - item: i7, dinootłʼizh, cube dóó aooʼ
  - item: i1, dinootłʼizh, cube dóó no
  - item: i3, dootłʼizh, cone dóó no
  - item: i2, dootłʼizh, cone dóó no
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - predict aap if: theory1, qalipaat aamma aappalajaarpoq
  - theory1 4 theory size
  - predict aap if: theory0, shape aamma cone
  - predict aap if: theory1, qalipaat aamma qorsuk
  - theory3 2 theory size
  - predict aap if: theory2, shape aamma cone
  - predict aap if: theory3, shape aamma cube
  - theory0 4 theory size
  - predict aap if: theory2, shape aamma cube
  - theory2 4 theory size
  - predict aap if: theory0, shape aamma cone
Cost model: exception cost 4.
Data:
  - item: i0, aappalajaarpoq, cone aamma aap
  - item: i5, qorsuk, cube aamma no
  - item: i4, qorsuk, cube aamma aap
  - item: i6, tungujortoq, sphere aamma aap
  - item: i7, qorsuk, cube aamma aap
  - item: i1, qorsuk, cube aamma no
  - item: i3, tungujortoq, cone aamma no
  - item: i2, tungujortoq, cone aamma no
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ya sapos: theory1, kala na ret
  - theory1 theory size 4
  - predict ya sapos: theory0, shape na cone
  - predict ya sapos: theory1, kala na grinpela
  - theory3 theory size 2
  - predict ya sapos: theory2, shape na cone
  - predict ya sapos: theory3, shape na cube
  - theory0 theory size 4
  - predict ya sapos: theory2, shape na cube
  - theory2 theory size 4
  - predict ya sapos: theory0, shape na cone
Cost model: exception cost 4.
Data:
  - item: i0, ret, cone na ya
  - item: i5, grinpela, cube na no
  - item: i4, grinpela, cube na ya
  - item: i6, blu, sphere na ya
  - item: i7, grinpela, cube na ya
  - item: i1, grinpela, cube na no
  - item: i3, blu, cone na no
  - item: i2, blu, cone na no
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, color màxke
  - theory1 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 theory size 4
  - predict yes if: theory2, shape cube
  - theory2 theory size 4
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, màxke, cone yes
  - item: i5, green, cube no
  - item: i4, green, cube yes
  - item: i6, blue, sphere yes
  - item: i7, green, cube yes
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - predict yes if: theory1, бос а цӏе
  - theory1 theory size 4
  - predict yes if: theory0, shape а cone
  - predict yes if: theory1, бос а баьццара
  - theory3 theory size 2
  - predict yes if: theory2, shape а cone
  - predict yes if: theory3, shape а cube
  - theory0 theory size 4
  - predict yes if: theory2, shape а cube
  - theory2 theory size 4
  - predict yes if: theory0, shape а cone
Cost model: exception cost 4.
Data:
  - item: i0, цӏе, cone а yes
  - item: i5, баьццара, cube а no
  - item: i4, баьццара, cube а yes
  - item: i6, blue, sphere а yes
  - item: i7, баьццара, cube а yes
  - item: i1, баьццара, cube а no
  - item: i3, blue, cone а no
  - item: i2, blue, cone а no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - 예언하다 네 만약: theory1, 색깔 빨갛다
  - theory1 4 이론 크기
  - 예언하다 네 만약: theory0, 모양 솔방울
  - 예언하다 네 만약: theory1, 색깔 녹색의
  - theory3 2 이론 크기
  - 예언하다 네 만약: theory2, 모양 솔방울
  - 예언하다 네 만약: theory3, 모양 정육면체
  - theory0 4 이론 크기
  - 예언하다 네 만약: theory2, 모양 정육면체
  - theory2 4 이론 크기
  - 예언하다 네 만약: theory0, 모양 솔방울
cost model: 예외 cost 4.
자료:
  - 아이템: i0, 빨갛다, 솔방울 네
  - 아이템: i5, 녹색의, 정육면체 아니요
  - 아이템: i4, 녹색의, 정육면체 네
  - 아이템: i6, 푸르다, 구체 네
  - 아이템: i7, 녹색의, 정육면체 네
  - 아이템: i1, 녹색의, 정육면체 아니요
  - 아이템: i3, 푸르다, 솔방울 아니요
  - 아이템: i2, 푸르다, 솔방울 아니요
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - aurresan bai if: theory1, kolore eta gorri
  - theory1 4 theory tamaina
  - aurresan bai if: theory0, shape eta pinaburu
  - aurresan bai if: theory1, kolore eta berde
  - theory3 2 theory tamaina
  - aurresan bai if: theory2, shape eta pinaburu
  - aurresan bai if: theory3, shape eta kubo
  - theory0 4 theory tamaina
  - aurresan bai if: theory2, shape eta kubo
  - theory2 4 theory tamaina
  - aurresan bai if: theory0, shape eta pinaburu
Cost model: exception kostatu 4.
Data:
  - item: i0, gorri, pinaburu eta bai
  - item: i5, berde, kubo eta no
  - item: i4, berde, kubo eta bai
  - item: i6, urdin, esfera eta bai
  - item: i7, berde, kubo eta bai
  - item: i1, berde, kubo eta no
  - item: i3, urdin, pinaburu eta no
  - item: i2, urdin, pinaburu eta no
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - theory1 theory size 4
  - predict ᎥᎥ if: theory0, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory3 theory size 2
  - predict ᎥᎥ if: theory2, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory3, shape ᎠᎴ cube
  - theory0 theory size 4
  - predict ᎥᎥ if: theory2, shape ᎠᎴ cube
  - theory2 theory size 4
  - predict ᎥᎥ if: theory0, shape ᎠᎴ cone
Cost model: exception cost 4.
Data:
  - item: i0, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i4, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i6, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᎥᎥ
  - item: i7, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i1, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i3, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᏝᏃ
  - item: i2, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᏝᏃ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict arí if: theory1, llimphi puka
  - theory1 theory chhika 4
  - predict arí if: theory0, shape cone
  - predict arí if: theory1, llimphi waylla
  - theory3 theory chhika 2
  - predict arí if: theory2, shape cone
  - predict arí if: theory3, shape cube
  - theory0 theory chhika 4
  - predict arí if: theory2, shape cube
  - theory2 theory chhika 4
  - predict arí if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, puka, cone arí
  - item: i5, waylla, cube mánan
  - item: i4, waylla, cube arí
  - item: i6, anqas, sphere arí
  - item: i7, waylla, cube arí
  - item: i1, waylla, cube mánan
  - item: i3, anqas, cone mánan
  - item: i2, anqas, cone mánan
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, color ыкӏи плъыжьы
  - theory1 4 тиориэ size
  - predict yes if: theory0, shape ыкӏи cone
  - predict yes if: theory1, color ыкӏи уцышъо
  - theory3 2 тиориэ size
  - predict yes if: theory2, shape ыкӏи cone
  - predict yes if: theory3, shape ыкӏи cube
  - theory0 4 тиориэ size
  - predict yes if: theory2, shape ыкӏи cube
  - theory2 4 тиориэ size
  - predict yes if: theory0, shape ыкӏи cone
Cost model: exception cost 4.
Data:
  - item: i0, плъыжьы, cone ыкӏи yes
  - item: i5, уцышъо, cube ыкӏи no
  - item: i4, уцышъо, cube ыкӏи yes
  - item: i6, blue, sphere ыкӏи yes
  - item: i7, уцышъо, cube ыкӏи yes
  - item: i1, уцышъо, cube ыкӏи no
  - item: i3, blue, cone ыкӏи no
  - item: i2, blue, cone ыкӏи no
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - predict yes if: theory1, xim thiab liab
  - theory1 theory size 4
  - predict yes if: theory0, shape thiab cone
  - predict yes if: theory1, xim thiab ntsuab
  - theory3 theory size 2
  - predict yes if: theory2, shape thiab cone
  - predict yes if: theory3, shape thiab cube
  - theory0 theory size 4
  - predict yes if: theory2, shape thiab cube
  - theory2 theory size 4
  - predict yes if: theory0, shape thiab cone
Cost model: exception nqi 4.
Data:
  - item: i0, liab, cone thiab yes
  - item: i5, ntsuab, cube thiab no
  - item: i4, ntsuab, cube thiab yes
  - item: i6, xiav, sphere thiab yes
  - item: i7, ntsuab, cube thiab yes
  - item: i1, ntsuab, cube thiab no
  - item: i3, xiav, cone thiab no
  - item: i2, xiav, cone thiab no
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - predict ngai if: theory1, color red
  - theory1 theory size 4
  - predict ngai if: theory0, shape cone
  - predict ngai if: theory1, color green
  - theory3 theory size 2
  - predict ngai if: theory2, shape cone
  - predict ngai if: theory3, shape cube
  - theory0 theory size 4
  - predict ngai if: theory2, shape cube
  - theory2 theory size 4
  - predict ngai if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone ngai
  - item: i5, green, cube woi
  - item: i4, green, cube ngai
  - item: i6, blue, sphere ngai
  - item: i7, green, cube ngai
  - item: i1, green, cube woi
  - item: i3, blue, cone woi
  - item: i2, blue, cone woi
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - predict i̠i if: theory1, color micha' homa
  - theory1 theory size 4
  - predict i̠i if: theory0, shape micha' cone
  - predict i̠i if: theory1, color micha' green
  - theory3 theory size 2
  - predict i̠i if: theory2, shape micha' cone
  - predict i̠i if: theory3, shape micha' cube
  - theory0 theory size 4
  - predict i̠i if: theory2, shape micha' cube
  - theory2 theory size 4
  - predict i̠i if: theory0, shape micha' cone
Cost model: exception cost 4.
Data:
  - item: i0, homa, cone micha' i̠i
  - item: i5, green, cube micha' no
  - item: i4, green, cube micha' i̠i
  - item: i6, blue, sphere micha' i̠i
  - item: i7, green, cube micha' i̠i
  - item: i1, green, cube micha' no
  - item: i3, blue, cone micha' no
  - item: i2, blue, cone micha' no
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - predict yes if: theory1, color カ フレ
  - theory1 4 theory size
  - predict yes if: theory0, shape カ cone
  - predict yes if: theory1, color カ green
  - theory3 2 theory size
  - predict yes if: theory2, shape カ cone
  - predict yes if: theory3, shape カ cube
  - theory0 4 theory size
  - predict yes if: theory2, shape カ cube
  - theory2 4 theory size
  - predict yes if: theory0, shape カ cone
Cost model: exception cost 4.
Data:
  - item: i0, フレ, cone カ yes
  - item: i5, green, cube カ ソモ
  - item: i4, green, cube カ yes
  - item: i6, blue, sphere カ yes
  - item: i7, green, cube カ yes
  - item: i1, green, cube カ ソモ
  - item: i3, blue, cone カ ソモ
  - item: i2, blue, cone カ ソモ
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes эвыр: theory1, пэран ынкъам чеԓгыԓьын
  - theory1 theory size 4
  - predict yes эвыр: theory0, shape ынкъам cone
  - predict yes эвыр: theory1, пэран ынкъам green
  - theory3 theory size 2
  - predict yes эвыр: theory2, shape ынкъам cone
  - predict yes эвыр: theory3, shape ынкъам cube
  - theory0 theory size 4
  - predict yes эвыр: theory2, shape ынкъам cube
  - theory2 theory size 4
  - predict yes эвыр: theory0, shape ынкъам cone
Cost model: exception cost 4.
Data:
  - item: i0, чеԓгыԓьын, cone ынкъам yes
  - item: i5, green, cube ынкъам no
  - item: i4, green, cube ынкъам yes
  - item: i6, ныԓиԓиԓӄин, sphere ынкъам yes
  - item: i7, green, cube ынкъам yes
  - item: i1, green, cube ынкъам no
  - item: i3, ныԓиԓиԓӄин, cone ынкъам no
  - item: i2, ныԓиԓиԓӄин, cone ынкъам no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes wa: theory1, color yéetel chak
  - theory1 theory size 4
  - predict yes wa: theory0, shape yéetel cone
  - predict yes wa: theory1, color yéetel yaʼax
  - theory3 theory size 2
  - predict yes wa: theory2, shape yéetel cone
  - predict yes wa: theory3, shape yéetel cube
  - theory0 theory size 4
  - predict yes wa: theory2, shape yéetel cube
  - theory2 theory size 4
  - predict yes wa: theory0, shape yéetel cone
Cost model: exception cost 4.
Data:
  - item: i0, chak, cone yéetel yes
  - item: i5, yaʼax, cube yéetel no
  - item: i4, yaʼax, cube yéetel yes
  - item: i6, blue, sphere yéetel yes
  - item: i7, yaʼax, cube yéetel yes
  - item: i1, yaʼax, cube yéetel no
  - item: i3, blue, cone yéetel no
  - item: i2, blue, cone yéetel no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict hą if: theory1, color red
  - theory1 theory size 4
  - predict hą if: theory0, shape cone
  - predict hą if: theory1, color green
  - theory3 theory size 2
  - predict hą if: theory2, shape cone
  - predict hą if: theory3, shape cube
  - theory0 theory size 4
  - predict hą if: theory2, shape cube
  - theory2 theory size 4
  - predict hą if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone hą
  - item: i5, green, cube no
  - item: i4, green, cube hą
  - item: i6, blue, sphere hą
  - item: i7, green, cube hą
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - predict yes if: theory1, color na red
  - theory1 4 theory size
  - predict yes if: theory0, shape na cone
  - predict yes if: theory1, color na green
  - theory3 2 theory size
  - predict yes if: theory2, shape na cone
  - predict yes if: theory3, shape na cube
  - theory0 4 theory size
  - predict yes if: theory2, shape na cube
  - theory2 4 theory size
  - predict yes if: theory0, shape na cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone na yes
  - item: i5, green, cube na no
  - item: i4, green, cube na yes
  - item: i6, blue, sphere na yes
  - item: i7, green, cube na yes
  - item: i1, green, cube na no
  - item: i3, blue, cone na no
  - item: i2, blue, cone na no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict heẽ if: theory1, color pytã
  - theory1 theory size 4
  - predict heẽ if: theory0, shape cone
  - predict heẽ if: theory1, color green
  - theory3 theory size 2
  - predict heẽ if: theory2, shape cone
  - predict heẽ if: theory3, shape cube
  - theory0 theory size 4
  - predict heẽ if: theory2, shape cube
  - theory2 theory size 4
  - predict heẽ if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, pytã, cone heẽ
  - item: i5, green, cube nahániri
  - item: i4, green, cube heẽ
  - item: i6, blue, sphere heẽ
  - item: i7, green, cube heẽ
  - item: i1, green, cube nahániri
  - item: i3, blue, cone nahániri
  - item: i2, blue, cone nahániri
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict jisa if: theory1, sami chupika
  - theory1 4 theory size
  - predict jisa if: theory0, shape cone
  - predict jisa if: theory1, sami ch’uxña
  - theory3 2 theory size
  - predict jisa if: theory2, shape cone
  - predict jisa if: theory3, shape cube
  - theory0 4 theory size
  - predict jisa if: theory2, shape cube
  - theory2 4 theory size
  - predict jisa if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, chupika, cone jisa
  - item: i5, ch’uxña, cube no
  - item: i4, ch’uxña, cube jisa
  - item: i6, larama, sphere jisa
  - item: i7, ch’uxña, cube jisa
  - item: i1, ch’uxña, cube no
  - item: i3, larama, cone no
  - item: i2, larama, cone no
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ʻée if: theory1, color ǂʻá red
  - theory1 theory ʻǁnàhã 4
  - predict ʻée if: theory0, shape ǂʻá cone
  - predict ʻée if: theory1, color ǂʻá green
  - theory3 theory ʻǁnàhã 2
  - predict ʻée if: theory2, shape ǂʻá cone
  - predict ʻée if: theory3, shape ǂʻá cube
  - theory0 theory ʻǁnàhã 4
  - predict ʻée if: theory2, shape ǂʻá cube
  - theory2 theory ʻǁnàhã 4
  - predict ʻée if: theory0, shape ǂʻá cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone ǂʻá ʻée
  - item: i5, green, cube ǂʻá ʻĩĩ
  - item: i4, green, cube ǂʻá ʻée
  - item: i6, blue, sphere ǂʻá ʻée
  - item: i7, green, cube ǂʻá ʻée
  - item: i1, green, cube ǂʻá ʻĩĩ
  - item: i3, blue, cone ǂʻá ʻĩĩ
  - item: i2, blue, cone ǂʻá ʻĩĩ
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, вал red
  - theory1 4 theory size
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, вал green
  - theory3 2 theory size
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 4 theory size
  - predict yes if: theory2, shape cube
  - theory2 4 theory size
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone yes
  - item: i5, green, cube no
  - item: i4, green, cube yes
  - item: i6, blue, sphere yes
  - item: i7, green, cube yes
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, color red
  - theory1 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 theory size 4
  - predict yes if: theory2, shape cube
  - theory2 theory size 4
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone yes
  - item: i5, green, cube no
  - item: i4, green, cube yes
  - item: i6, blue, sphere yes
  - item: i7, green, cube yes
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, color red
  - theory1 4 theory size
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, color green
  - theory3 2 theory size
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 4 theory size
  - predict yes if: theory2, shape cube
  - theory2 4 theory size
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone yes
  - item: i5, green, cube no
  - item: i4, green, cube yes
  - item: i6, blue, sphere yes
  - item: i7, green, cube yes
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ee if: theory1, color red
  - theory1 theory size 4
  - predict ee if: theory0, shape cone
  - predict ee if: theory1, color green
  - theory3 theory size 2
  - predict ee if: theory2, shape cone
  - predict ee if: theory3, shape cube
  - theory0 theory size 4
  - predict ee if: theory2, shape cube
  - theory2 theory size 4
  - predict ee if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone ee
  - item: i5, green, cube no
  - item: i4, green, cube ee
  - item: i6, blue, sphere ee
  - item: i7, green, cube ee
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, color red
  - theory1 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 theory size 4
  - predict yes if: theory2, shape cube
  - theory2 theory size 4
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone yes
  - item: i5, green, cube inte
  - item: i4, green, cube yes
  - item: i6, blue, sphere yes
  - item: i7, green, cube yes
  - item: i1, green, cube inte
  - item: i3, blue, cone inte
  - item: i2, blue, cone inte
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - prédire oui si: theory1, couleur et rouge
  - theory1 théorie taille 4
  - prédire oui si: theory0, forme et cône
  - prédire oui si: theory1, couleur et vert
  - theory3 théorie taille 2
  - prédire oui si: theory2, forme et cône
  - prédire oui si: theory3, forme et cube
  - theory0 théorie taille 4
  - prédire oui si: theory2, forme et cube
  - theory2 théorie taille 4
  - prédire oui si: theory0, forme et cône
Coûts : exception coûter 4.
Les données sont:
  - article: i0, rouge, cône et oui
  - article: i5, vert, cube et no
  - article: i4, vert, cube et oui
  - article: i6, bleu, sphère et oui
  - article: i7, vert, cube et oui
  - article: i1, vert, cube et no
  - article: i3, bleu, cône et no
  - article: i2, bleu, cône et no
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - predict jes se: theory1, koloro kaj ruĝa
  - theory1 teorio grandeco 4
  - predict jes se: theory0, formi kaj strobilo
  - predict jes se: theory1, koloro kaj verda
  - theory3 teorio grandeco 2
  - predict jes se: theory2, formi kaj strobilo
  - predict jes se: theory3, formi kaj kubo
  - theory0 teorio grandeco 4
  - predict jes se: theory2, formi kaj kubo
  - theory2 teorio grandeco 4
  - predict jes se: theory0, formi kaj strobilo
Kosto pozistino: escepto kosti 4.
Datumo:
  - ero: i0, ruĝa, strobilo kaj jes
  - ero: i5, verda, kubo kaj no
  - ero: i4, verda, kubo kaj jes
  - ero: i6, blua, sfero kaj jes
  - ero: i7, verda, kubo kaj jes
  - ero: i1, verda, kubo kaj no
  - ero: i3, blua, strobilo kaj no
  - ero: i2, blua, strobilo kaj no
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - megjósol igen ha: theory1, szín és piros
  - theory1 elmélet méret 4
  - megjósol igen ha: theory0, alak és toboz
  - megjósol igen ha: theory1, szín és zöld
  - theory3 elmélet méret 2
  - megjósol igen ha: theory2, alak és toboz
  - megjósol igen ha: theory3, alak és kocka
  - theory0 elmélet méret 4
  - megjósol igen ha: theory2, alak és kocka
  - theory2 elmélet méret 4
  - megjósol igen ha: theory0, alak és toboz
Költség manöken: kivétel kerül 4.
Adat:
  - cikk: i0, piros, toboz és igen
  - cikk: i5, zöld, kocka és no
  - cikk: i4, zöld, kocka és igen
  - cikk: i6, kék, gömb és igen
  - cikk: i7, zöld, kocka és igen
  - cikk: i1, zöld, kocka és no
  - cikk: i3, kék, toboz és no
  - cikk: i2, kék, toboz és no
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - predict係要是:theory1, 色及紅
  - theory1theory size4
  - predict係要是:theory0, 形狀及cone
  - predict係要是:theory1, 色及綠色嘅
  - theory3theory size2
  - predict係要是:theory2, 形狀及cone
  - predict係要是:theory3, 形狀及cube
  - theory0theory size4
  - predict係要是:theory2, 形狀及cube
  - theory2theory size4
  - predict係要是:theory0, 形狀及cone
cost model:exception cost:4.
數據:
  - item:i0, 紅, cone及係
  - item:i5, 綠色嘅, cube及no
  - item:i4, 綠色嘅, cube及係
  - item:i6, 藍色嘅, sphere及係
  - item:i7, 綠色嘅, cube及係
  - item:i1, 綠色嘅, cube及no
  - item:i3, 藍色嘅, cone及no
  - item:i2, 藍色嘅, cone及no
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - predict yes if: theory1, color とぅ red
  - theory1 theory size 4
  - predict yes if: theory0, shape とぅ cone
  - predict yes if: theory1, color とぅ green
  - theory3 theory size 2
  - predict yes if: theory2, shape とぅ cone
  - predict yes if: theory3, shape とぅ cube
  - theory0 theory size 4
  - predict yes if: theory2, shape とぅ cube
  - theory2 theory size 4
  - predict yes if: theory0, shape とぅ cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone とぅ yes
  - item: i5, green, cube とぅ no
  - item: i4, green, cube とぅ yes
  - item: i6, blue, sphere とぅ yes
  - item: i7, green, cube とぅ yes
  - item: i1, green, cube とぅ no
  - item: i3, blue, cone とぅ no
  - item: i2, blue, cone とぅ no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - predict иә егер: theory1, түс және қызыл
  - theory1 теория мөлшер 4
  - predict иә егер: theory0, нысан және бүршік
  - predict иә егер: theory1, түс және жасыл
  - theory3 теория мөлшер 2
  - predict иә егер: theory2, нысан және бүршік
  - predict иә егер: theory3, нысан және куб
  - theory0 теория мөлшер 4
  - predict иә егер: theory2, нысан және куб
  - theory2 теория мөлшер 4
  - predict иә егер: theory0, нысан және бүршік
Тұру үлгі: ерекшелік тұру 4.
Деректер:
  - зат: i0, қызыл, бүршік және иә
  - зат: i5, жасыл, куб және жоқ
  - зат: i4, жасыл, куб және иә
  - зат: i6, көк, сфера және иә
  - зат: i7, жасыл, куб және иә
  - зат: i1, жасыл, куб және жоқ
  - зат: i3, көк, бүршік және жоқ
  - зат: i2, көк, бүршік және жоқ
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - predict כֵּן אִם: theory1, צֶבַע וְ־ אָדוֹם
  - theory1 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory0, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory1, צֶבַע וְ־ ירוק מקנאה
  - theory3 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory2, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory3, צוּרָה וְ־ קובייה
  - theory0 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory2, צוּרָה וְ־ קובייה
  - theory2 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory0, צוּרָה וְ־ אצטרובל
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 4.
נְתוּנִים:
  - אבזר: i0, אָדוֹם, אצטרובל וְ־ כֵּן
  - אבזר: i5, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i4, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i6, כָּחוֹל, כַּדּוּר וְ־ כֵּן
  - אבזר: i7, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i1, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i3, כָּחוֹל, אצטרובל וְ־ no
  - אבזר: i2, כָּחוֹל, אצטרובל וְ־ no
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - predict იახ if: theory1, color დო red
  - theory1 theory size 4
  - predict იახ if: theory0, shape დო cone
  - predict იახ if: theory1, color დო green
  - theory3 theory size 2
  - predict იახ if: theory2, shape დო cone
  - predict იახ if: theory3, shape დო cube
  - theory0 theory size 4
  - predict იახ if: theory2, shape დო cube
  - theory2 theory size 4
  - predict იახ if: theory0, shape დო cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone დო იახ
  - item: i5, green, cube დო no
  - item: i4, green, cube დო იახ
  - item: i6, blue, sphere დო იახ
  - item: i7, green, cube დო იახ
  - item: i1, green, cube დო no
  - item: i3, blue, cone დო no
  - item: i2, blue, cone დო no
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - predict ya jika: theory1, warna dan merah
  - theory1 teori ukuran 4
  - predict ya jika: theory0, bentuk dan kerucut
  - predict ya jika: theory1, warna dan hijau
  - theory3 teori ukuran 2
  - predict ya jika: theory2, bentuk dan kerucut
  - predict ya jika: theory3, bentuk dan kubus
  - theory0 teori ukuran 4
  - predict ya jika: theory2, bentuk dan kubus
  - theory2 teori ukuran 4
  - predict ya jika: theory0, bentuk dan kerucut
Cost model: pengecualian menghabiskan biaya 4.
Data:
  - benda: i0, merah, kerucut dan ya
  - benda: i5, hijau, kubus dan no
  - benda: i4, hijau, kubus dan ya
  - benda: i6, biru, bola dan ya
  - benda: i7, hijau, kubus dan ya
  - benda: i1, hijau, kubus dan no
  - benda: i3, biru, kerucut dan no
  - benda: i2, biru, kerucut dan no
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងក្រហម
  - theory1ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory0, គ្រោងនឹងកោណ
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងបៃតង
  - theory3ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory2, គ្រោងនឹងកោណ
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគូប
  - theory0ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory2, គ្រោងនឹងគូប
  - theory2ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory0, គ្រោងនឹងកោណ
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:4.
ទិន្នន័យ:
  - item:i0, ក្រហម, កោណនឹងច៎ា
  - item:i5, បៃតង, គូបនឹងទេ
  - item:i4, បៃតង, គូបនឹងច៎ា
  - item:i6, ខៀវ, គោលនឹងច៎ា
  - item:i7, បៃតង, គូបនឹងច៎ា
  - item:i1, បៃតង, គូបនឹងទេ
  - item:i3, ខៀវ, កោណនឹងទេ
  - item:i2, ខៀវ, កោណនឹងទេ
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະແດງ
  - theory1ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ຮູບຮ່າງແລະcone
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຂຽວ
  - theory3ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ຮູບຮ່າງແລະcone
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະລູກບາດ
  - theory0ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ຮູບຮ່າງແລະລູກບາດ
  - theory2ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ຮູບຮ່າງແລະcone
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:4.
ຂໍ້ມູນ:
  - item:i0, ແດງ, coneແລະແມ່ນ
  - item:i5, ຂຽວ, ລູກບາດແລະno
  - item:i4, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i6, ຟ້າ, ຄະດີແລະແມ່ນ
  - item:i7, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i1, ຂຽວ, ລູກບາດແລະno
  - item:i3, ຟ້າ, coneແລະno
  - item:i2, ຟ້າ, coneແລະno
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - predict అవును అయితే: theory1, రంగు మరియు ఎర్ర
  - theory1 4 theory size
  - predict అవును అయితే: theory0, ఆకారం మరియు cone
  - predict అవును అయితే: theory1, రంగు మరియు పచ్చని
  - theory3 2 theory size
  - predict అవును అయితే: theory2, ఆకారం మరియు cone
  - predict అవును అయితే: theory3, ఆకారం మరియు cube
  - theory0 4 theory size
  - predict అవును అయితే: theory2, ఆకారం మరియు cube
  - theory2 4 theory size
  - predict అవును అయితే: theory0, ఆకారం మరియు cone
cost model: exception cost 4.
data:
  - item: i0, ఎర్ర, cone మరియు అవును
  - item: i5, పచ్చని, cube మరియు no
  - item: i4, పచ్చని, cube మరియు అవును
  - item: i6, నీలము, గోళము మరియు అవును
  - item: i7, పచ్చని, cube మరియు అవును
  - item: i1, పచ్చని, cube మరియు no
  - item: i3, నీలము, cone మరియు no
  - item: i2, నీలము, cone మరియు no
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - predict hẹn if: theory1, color pupa
  - theory1 theory size 4
  - predict hẹn if: theory0, shape cone
  - predict hẹn if: theory1, color green
  - theory3 theory size 2
  - predict hẹn if: theory2, shape cone
  - predict hẹn if: theory3, shape cube
  - theory0 theory size 4
  - predict hẹn if: theory2, shape cube
  - theory2 theory size 4
  - predict hẹn if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, pupa, cone bẹ́ẹ̀ ni
  - item: i5, green, cube rárá
  - item: i4, green, cube bẹ́ẹ̀ ni
  - item: i6, dúdú, sphere bẹ́ẹ̀ ni
  - item: i7, green, cube bẹ́ẹ̀ ni
  - item: i1, green, cube rárá
  - item: i3, dúdú, cone rárá
  - item: i2, dúdú, cone rárá
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict au if: theory1, color łichíí
  - theory1 4 theory size
  - predict au if: theory0, shape cone
  - predict au if: theory1, color green
  - theory3 2 theory size
  - predict au if: theory2, shape cone
  - predict au if: theory3, shape cube
  - theory0 4 theory size
  - predict au if: theory2, shape cube
  - theory2 4 theory size
  - predict au if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, łichíí, cone au
  - item: i5, green, cube no
  - item: i4, green, cube au
  - item: i6, blue, sphere au
  - item: i7, green, cube au
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - predict yes if: theory1, color red
  - theory1 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 theory size 4
  - predict yes if: theory2, shape cube
  - theory2 theory size 4
  - predict yes if: theory0, shape cone
cost model: exception cost 4.
data:
  - item: i0, red, cone yes
  - item: i5, green, cube ᐋᒃᑲ
  - item: i4, green, cube yes
  - item: i6, blue, sphere yes
  - item: i7, green, cube yes
  - item: i1, green, cube ᐋᒃᑲ
  - item: i3, blue, cone ᐋᒃᑲ
  - item: i2, blue, cone ᐋᒃᑲ
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - predict yes if: theory1, color red
  - theory1 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, color grun
  - theory3 theory size 2
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 theory size 4
  - predict yes if: theory2, shape cube
  - theory2 theory size 4
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone yes
  - item: i5, grun, cube no
  - item: i4, grun, cube yes
  - item: i6, blaw, sphere yes
  - item: i7, grun, cube yes
  - item: i1, grun, cube no
  - item: i3, blaw, cone no
  - item: i2, blaw, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, color red
  - theory1 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 theory size 4
  - predict yes if: theory2, shape cube
  - theory2 theory size 4
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone yes
  - item: i5, green, cube no
  - item: i4, green, cube yes
  - item: i6, blue, sphere yes
  - item: i7, green, cube yes
  - item: i1, green, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, ранг red
  - theory1 4 theory size
  - predict yes if: theory0, shape cone
  - predict yes if: theory1, ранг къацу
  - theory3 2 theory size
  - predict yes if: theory2, shape cone
  - predict yes if: theory3, shape cube
  - theory0 4 theory size
  - predict yes if: theory2, shape cube
  - theory2 4 theory size
  - predict yes if: theory0, shape cone
Cost model: exception cost 4.
Data:
  - item: i0, red, cone yes
  - item: i5, къацу, cube no
  - item: i4, къацу, cube yes
  - item: i6, blue, sphere yes
  - item: i7, къацу, cube yes
  - item: i1, къацу, cube no
  - item: i3, blue, cone no
  - item: i2, blue, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - predicts yes if: theory2, colour and green
  - predicts yes if: theory2, shape and cube
  - predicts yes if: theory0, colour and blue
  - predicts yes if: theory3, colour and blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predicts yes if: theory3, colour and red
  - theory2 theory size 4
  - predicts yes if: theory1, shape and cone
  - predicts yes if: theory1, shape and cube
  - theory1 theory size 4
Costs: exception cost 3.
The data are:
  - item: i0, red, sphere and no
  - item: i5, blue, cone and no
  - item: i1, red, cone and yes
  - item: i2, blue, sphere and yes
  - item: i4, red, cube and yes
  - item: i6, blue, cube and no
  - item: i7, red, cone and no
  - item: i3, red, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - predice sí si: theory2, es de color y verde
  - predice sí si: theory2, forma y cubo
  - predice sí si: theory0, es de color y azul
  - predice sí si: theory3, es de color y azul
  - theory3 el tamaño de la teoría 4
  - theory0 el tamaño de la teoría 2
  - predice sí si: theory3, es de color y rojo
  - theory2 el tamaño de la teoría 4
  - predice sí si: theory1, forma y cono
  - predice sí si: theory1, forma y cubo
  - theory1 el tamaño de la teoría 4
Costes: exception el coste: 3.
Los datos son:
  - el elemento: i0, rojo, esfera y no
  - el elemento: i5, azul, cono y no
  - el elemento: i1, rojo, cono y sí
  - el elemento: i2, azul, esfera y sí
  - el elemento: i4, rojo, cubo y sí
  - el elemento: i6, azul, cubo y no
  - el elemento: i7, rojo, cono y no
  - el elemento: i3, rojo, cubo y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - 预测是若：theory2、颜色和绿色的
  - 预测是若：theory2、形状和立方体
  - 预测是若：theory0、颜色和蓝色的
  - 预测是若：theory3、颜色和蓝色的
  - theory3的理论规模是4
  - theory0的理论规模是2
  - 预测是若：theory3、颜色和红色的
  - theory2的理论规模是4
  - 预测是若：theory1、形状和圆锥
  - 预测是若：theory1、形状和立方体
  - theory1的理论规模是4
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i0、红色的、球体和否
  - 的物品是：i5、蓝色的、圆锥和否
  - 的物品是：i1、红色的、圆锥和是
  - 的物品是：i2、蓝色的、球体和是
  - 的物品是：i4、红色的、立方体和是
  - 的物品是：i6、蓝色的、立方体和否
  - 的物品是：i7、红色的、圆锥和否
  - 的物品是：i3、红色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - öngörür evet eğer: theory2, renk ve yeşil
  - öngörür evet eğer: theory2, şekil ve küp
  - öngörür evet eğer: theory0, renk ve mavi
  - öngörür evet eğer: theory3, renk ve mavi
  - theory3 4 kuram boyut
  - theory0 2 kuram boyut
  - öngörür evet eğer: theory3, renk ve kırmızı
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory1, şekil ve koni
  - öngörür evet eğer: theory1, şekil ve küp
  - theory1 4 kuram boyut
Cost model: exception maliyet: 3.
Veriler:
  - item: i0, kırmızı, küre ve hayır
  - item: i5, mavi, koni ve hayır
  - item: i1, kırmızı, koni ve evet
  - item: i2, mavi, küre ve evet
  - item: i4, kırmızı, küp ve evet
  - item: i6, mavi, küp ve hayır
  - item: i7, kırmızı, koni ve hayır
  - item: i3, kırmızı, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na kijani
  - predict ndiyo kama: theory2, umbo na mchemraba
  - predict ndiyo kama: theory0, rangi na buluu
  - predict ndiyo kama: theory3, rangi na buluu
  - theory3 theory size 4
  - theory0 theory size 2
  - predict ndiyo kama: theory3, rangi na ekundu
  - theory2 theory size 4
  - predict ndiyo kama: theory1, umbo na koni
  - predict ndiyo kama: theory1, umbo na mchemraba
  - theory1 theory size 4
Cost model: exception gharimu: 3.
Data:
  - item: i0, ekundu, tufe na hapana
  - item: i5, buluu, koni na hapana
  - item: i1, ekundu, koni na ndiyo
  - item: i2, buluu, tufe na ndiyo
  - item: i4, ekundu, mchemraba na ndiyo
  - item: i6, buluu, mchemraba na hapana
  - item: i7, ekundu, koni na hapana
  - item: i3, ekundu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - predicts yes if: theory2, colour and green
  - predicts yes if: theory2, shape and cube
  - predicts yes if: theory0, colour and blue
  - predicts yes if: theory3, colour and blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predicts yes if: theory3, colour and red
  - theory2 theory size 4
  - predicts yes if: theory1, shape and cone
  - predicts yes if: theory1, shape and cube
  - theory1 theory size 4
Costs: exception cost 3.
The data are:
  - item: i0, red, sphere and no
  - item: i5, blue, cone and no
  - item: i1, red, cone and yes
  - item: i2, blue, sphere and yes
  - item: i4, red, cube and yes
  - item: i6, blue, cube and no
  - item: i7, red, cone and no
  - item: i3, red, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(predicts_yes_if theory2 color green) (predicts_yes_if theory2 shape cube) (predicts_yes_if theory0 color blue) (predicts_yes_if theory3 color blue) (theory_size theory3 4) (theory_size theory0 2) (predicts_yes_if theory3 color red) (theory_size theory2 4) (predicts_yes_if theory1 shape cone) (predicts_yes_if theory1 shape cube) (theory_size theory1 4)], cost_model: [(exception_cost 3)], data: [(item i0 red sphere no) (item i5 blue cone no) (item i1 red cone yes) (item i2 blue sphere yes) (item i4 red cube yes) (item i6 blue cube no) (item i7 red cone no) (item i3 red cube yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - vorhersagen ja wenn: theory2, Farbe und grün
  - vorhersagen ja wenn: theory2, Form und Kubus
  - vorhersagen ja wenn: theory0, Farbe und blau
  - vorhersagen ja wenn: theory3, Farbe und blau
  - theory3 Theorie Größe 4
  - theory0 Theorie Größe 2
  - vorhersagen ja wenn: theory3, Farbe und rot
  - theory2 Theorie Größe 4
  - vorhersagen ja wenn: theory1, Form und Kegel
  - vorhersagen ja wenn: theory1, Form und Kubus
  - theory1 Theorie Größe 4
Kosten: Ausnahme kosten 3.
Die Daten sind:
  - Ding: i0, rot, Kugel und nein
  - Ding: i5, blau, Kegel und nein
  - Ding: i1, rot, Kegel und ja
  - Ding: i2, blau, Kugel und ja
  - Ding: i4, rot, Kubus und ja
  - Ding: i6, blau, Kubus und nein
  - Ding: i7, rot, Kegel und nein
  - Ding: i3, rot, Kubus und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - predvidjeti да ȁko: theory2, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory2, о̏блӣк и коцка
  - predvidjeti да ȁko: theory0, бо̀ја и пла̑в
  - predvidjeti да ȁko: theory3, бо̀ја и пла̑в
  - theory3 тѐо̄рија величѝна 4
  - theory0 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory3, бо̀ја и цр̀вен
  - theory2 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory1, о̏блӣк и stožac
  - predvidjeti да ȁko: theory1, о̏блӣк и коцка
  - theory1 тѐо̄рија величѝна 4
Trošak мо̀дел: изузе́так ко̏штати 3.
Податци:
  - item: i0, цр̀вен, сфе́ра и no
  - item: i5, пла̑в, stožac и no
  - item: i1, цр̀вен, stožac и да
  - item: i2, пла̑в, сфе́ра и да
  - item: i4, цр̀вен, коцка и да
  - item: i6, пла̑в, коцка и no
  - item: i7, цр̀вен, stožac и no
  - item: i3, цр̀вен, коцка и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - ennakoida kyllä jos: theory2, väri ja vihreä
  - ennakoida kyllä jos: theory2, muoto ja kuutio
  - ennakoida kyllä jos: theory0, väri ja sininen
  - ennakoida kyllä jos: theory3, väri ja sininen
  - theory3 teoria koko 4
  - theory0 teoria koko 2
  - ennakoida kyllä jos: theory3, väri ja punainen
  - theory2 teoria koko 4
  - ennakoida kyllä jos: theory1, muoto ja kartio
  - ennakoida kyllä jos: theory1, muoto ja kuutio
  - theory1 teoria koko 4
Kustannus malli: poikkeus maksaa 3.
Tieto:
  - esine: i0, punainen, pallo ja ei
  - esine: i5, sininen, kartio ja ei
  - esine: i1, punainen, kartio ja kyllä
  - esine: i2, sininen, pallo ja kyllä
  - esine: i4, punainen, kuutio ja kyllä
  - esine: i6, sininen, kuutio ja ei
  - esine: i7, punainen, kartio ja ei
  - esine: i3, punainen, kuutio ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - 预测是若：theory2、颜色和绿色的
  - 预测是若：theory2、形状和立方体
  - 预测是若：theory0、颜色和蓝色的
  - 预测是若：theory3、颜色和蓝色的
  - theory3的理论规模是4
  - theory0的理论规模是2
  - 预测是若：theory3、颜色和红色的
  - theory2的理论规模是4
  - 预测是若：theory1、形状和圆锥
  - 预测是若：theory1、形状和立方体
  - theory1的理论规模是4
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i0、红色的、球体和否
  - 的物品是：i5、蓝色的、圆锥和否
  - 的物品是：i1、红色的、圆锥和是
  - 的物品是：i2、蓝色的、球体和是
  - 的物品是：i4、红色的、立方体和是
  - 的物品是：i6、蓝色的、立方体和否
  - 的物品是：i7、红色的、圆锥和否
  - 的物品是：i3、红色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - 予言するはいもし...:theory2, 色と緑の
  - 予言するはいもし...:theory2, 形と立方体
  - 予言するはいもし...:theory0, 色と青い
  - 予言するはいもし...:theory3, 色と青い
  - theory34理論大きさ
  - theory02理論大きさ
  - 予言するはいもし...:theory3, 色と赤い
  - theory24理論大きさ
  - 予言するはいもし...:theory1, 形と松毬
  - 予言するはいもし...:theory1, 形と立方体
  - theory14理論大きさ
費用モデル:例外費やす:3.
資料:
  - 項目:i0, 赤い, 球といいえ
  - 項目:i5, 青い, 松毬といいえ
  - 項目:i1, 赤い, 松毬とはい
  - 項目:i2, 青い, 球とはい
  - 項目:i4, 赤い, 立方体とはい
  - 項目:i6, 青い, 立方体といいえ
  - 項目:i7, 赤い, 松毬といいえ
  - 項目:i3, 赤い, 立方体とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - öngörür evet eğer: theory2, renk ve yeşil
  - öngörür evet eğer: theory2, şekil ve küp
  - öngörür evet eğer: theory0, renk ve mavi
  - öngörür evet eğer: theory3, renk ve mavi
  - theory3 4 kuram boyut
  - theory0 2 kuram boyut
  - öngörür evet eğer: theory3, renk ve kırmızı
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory1, şekil ve koni
  - öngörür evet eğer: theory1, şekil ve küp
  - theory1 4 kuram boyut
Cost model: exception maliyet: 3.
Veriler:
  - item: i0, kırmızı, küre ve hayır
  - item: i5, mavi, koni ve hayır
  - item: i1, kırmızı, koni ve evet
  - item: i2, mavi, küre ve evet
  - item: i4, kırmızı, küp ve evet
  - item: i6, mavi, küp ve hayır
  - item: i7, kırmızı, koni ve hayır
  - item: i3, kırmızı, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ كَعْبَة
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَزْرَق
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَزْرَق
  - نَظَرِيَّة مَقَاس theory3 4
  - نَظَرِيَّة مَقَاس theory0 2
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَحْمَر
  - نَظَرِيَّة مَقَاس theory2 4
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ كَعْبَة
  - نَظَرِيَّة مَقَاس theory1 4
cost model: اِسْتِثْنَاء cost 3.
بَيَانَات:
  - بَنْد: i0، أَحْمَر، كُرَة وَـ no
  - بَنْد: i5، أَزْرَق، مَخْرُوط وَـ no
  - بَنْد: i1، أَحْمَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i2، أَزْرَق، كُرَة وَـ نَعَم
  - بَنْد: i4، أَحْمَر، كَعْبَة وَـ نَعَم
  - بَنْد: i6، أَزْرَق، كَعْبَة وَـ no
  - بَنْد: i7، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i3، أَحْمَر، كَعْبَة وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - predict დიახ თუ: theory2, ფერი და მწვანე
  - predict დიახ თუ: theory2, ფორმა და კუბი
  - predict დიახ თუ: theory0, ფერი და ლურჯი
  - predict დიახ თუ: theory3, ფერი და ლურჯი
  - theory3 4 თეორია ზომა
  - theory0 2 თეორია ზომა
  - predict დიახ თუ: theory3, ფერი და წითელი
  - theory2 4 თეორია ზომა
  - predict დიახ თუ: theory1, ფორმა და გირჩა
  - predict დიახ თუ: theory1, ფორმა და კუბი
  - theory1 4 თეორია ზომა
Ფასი მოდელი: გამონაკლისი ღირს 3.
Მონაცემები:
  - item: i0, წითელი, სფერო და no
  - item: i5, ლურჯი, გირჩა და no
  - item: i1, წითელი, გირჩა და დიახ
  - item: i2, ლურჯი, სფერო და დიახ
  - item: i4, წითელი, კუბი და დიახ
  - item: i6, ლურჯი, კუბი და no
  - item: i7, წითელი, გირჩა და no
  - item: i3, წითელი, კუბი და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - matakite āe mēnā: theory2, kano me kākāriki
  - matakite āe mēnā: theory2, āhuahanga me mataono
  - matakite āe mēnā: theory0, kano me kahurangi
  - matakite āe mēnā: theory3, kano me kahurangi
  - ariā size theory3 4
  - ariā size theory0 2
  - matakite āe mēnā: theory3, kano me red
  - ariā size theory2 4
  - matakite āe mēnā: theory1, āhuahanga me koeko
  - matakite āe mēnā: theory1, āhuahanga me mataono
  - ariā size theory1 4
Cost model: hāungatanga cost 3.
Hōtuku:
  - tūemi: i0, red, poi me kāo
  - tūemi: i5, kahurangi, koeko me kāo
  - tūemi: i1, red, koeko me āe
  - tūemi: i2, kahurangi, poi me āe
  - tūemi: i4, red, mataono me āe
  - tūemi: i6, kahurangi, mataono me kāo
  - tūemi: i7, red, koeko me kāo
  - tūemi: i3, red, mataono me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - dự báo vâng nếu: theory2, màu và green
  - dự báo vâng nếu: theory2, hình dáng và lập phương
  - dự báo vâng nếu: theory0, màu và blue
  - dự báo vâng nếu: theory3, màu và blue
  - theory3 lý thuyết kích thước 4
  - theory0 lý thuyết kích thước 2
  - dự báo vâng nếu: theory3, màu và đỏ
  - theory2 lý thuyết kích thước 4
  - dự báo vâng nếu: theory1, hình dáng và nón
  - dự báo vâng nếu: theory1, hình dáng và lập phương
  - theory1 lý thuyết kích thước 4
Cost model: lệ ngoại cost 3.
Dữ liệu:
  - vật phẩm: i0, đỏ, mặt cầu và no
  - vật phẩm: i5, blue, nón và no
  - vật phẩm: i1, đỏ, nón và vâng
  - vật phẩm: i2, blue, mặt cầu và vâng
  - vật phẩm: i4, đỏ, lập phương và vâng
  - vật phẩm: i6, blue, lập phương và no
  - vật phẩm: i7, đỏ, nón và no
  - vật phẩm: i3, đỏ, lập phương và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - ทำนายใช่ถ้า หาก:theory2, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและลูกบาศก์
  - ทำนายใช่ถ้า หาก:theory0, สีและน้ำเงิน
  - ทำนายใช่ถ้า หาก:theory3, สีและน้ำเงิน
  - theory3ทฤษฎีขนาด4
  - theory0ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory3, สีและแดง
  - theory2ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและลูกบาศก์
  - theory1ทฤษฎีขนาด4
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:3.
ข้อมูล:
  - item:i0, แดง, ทรงกลมและไฮ้
  - item:i5, น้ำเงิน, กรวยและไฮ้
  - item:i1, แดง, กรวยและใช่
  - item:i2, น้ำเงิน, ทรงกลมและใช่
  - item:i4, แดง, ลูกบาศก์และใช่
  - item:i6, น้ำเงิน, ลูกบาศก์และไฮ้
  - item:i7, แดง, กรวยและไฮ้
  - item:i3, แดง, ลูกบาศก์และใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory2, shape மற்றும் cube
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் நீலம்
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் நீலம்
  - theory3 4 theory size
  - theory0 2 theory size
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் சிவப்பு
  - theory2 4 theory size
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cube
  - theory1 4 theory size
cost model: exception செலவு 3.
தரவுகள்:
  - item: i0, சிவப்பு, sphere மற்றும் no
  - item: i5, நீலம், cone மற்றும் no
  - item: i1, சிவப்பு, cone மற்றும் ஆம்
  - item: i2, நீலம், sphere மற்றும் ஆம்
  - item: i4, சிவப்பு, cube மற்றும் ஆம்
  - item: i6, நீலம், cube மற்றும் no
  - item: i7, சிவப்பு, cone மற்றும் no
  - item: i3, சிவப்பு, cube மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na kijani
  - predict ndiyo kama: theory2, umbo na mchemraba
  - predict ndiyo kama: theory0, rangi na buluu
  - predict ndiyo kama: theory3, rangi na buluu
  - theory3 theory size 4
  - theory0 theory size 2
  - predict ndiyo kama: theory3, rangi na ekundu
  - theory2 theory size 4
  - predict ndiyo kama: theory1, umbo na koni
  - predict ndiyo kama: theory1, umbo na mchemraba
  - theory1 theory size 4
Cost model: exception gharimu: 3.
Data:
  - item: i0, ekundu, tufe na hapana
  - item: i5, buluu, koni na hapana
  - item: i1, ekundu, koni na ndiyo
  - item: i2, buluu, tufe na ndiyo
  - item: i4, ekundu, mchemraba na ndiyo
  - item: i6, buluu, mchemraba na hapana
  - item: i7, ekundu, koni na hapana
  - item: i3, ekundu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó cube
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó dootłʼizh
  - theory3 4 theory size
  - theory0 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó łichííʼ
  - theory2 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cube
  - theory1 4 theory size
Cost model: exception bą́ą́h ílį́ 3.
Data:
  - item: i0, łichííʼ, sphere dóó no
  - item: i5, dootłʼizh, cone dóó no
  - item: i1, łichííʼ, cone dóó aooʼ
  - item: i2, dootłʼizh, sphere dóó aooʼ
  - item: i4, łichííʼ, cube dóó aooʼ
  - item: i6, dootłʼizh, cube dóó no
  - item: i7, łichííʼ, cone dóó no
  - item: i3, łichííʼ, cube dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - predict aap if: theory2, qalipaat aamma qorsuk
  - predict aap if: theory2, shape aamma cube
  - predict aap if: theory0, qalipaat aamma tungujortoq
  - predict aap if: theory3, qalipaat aamma tungujortoq
  - theory3 4 theory size
  - theory0 2 theory size
  - predict aap if: theory3, qalipaat aamma aappalajaarpoq
  - theory2 4 theory size
  - predict aap if: theory1, shape aamma cone
  - predict aap if: theory1, shape aamma cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, aappalajaarpoq, sphere aamma no
  - item: i5, tungujortoq, cone aamma no
  - item: i1, aappalajaarpoq, cone aamma aap
  - item: i2, tungujortoq, sphere aamma aap
  - item: i4, aappalajaarpoq, cube aamma aap
  - item: i6, tungujortoq, cube aamma no
  - item: i7, aappalajaarpoq, cone aamma no
  - item: i3, aappalajaarpoq, cube aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ya sapos: theory2, kala na grinpela
  - predict ya sapos: theory2, shape na cube
  - predict ya sapos: theory0, kala na blu
  - predict ya sapos: theory3, kala na blu
  - theory3 theory size 4
  - theory0 theory size 2
  - predict ya sapos: theory3, kala na ret
  - theory2 theory size 4
  - predict ya sapos: theory1, shape na cone
  - predict ya sapos: theory1, shape na cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, ret, sphere na no
  - item: i5, blu, cone na no
  - item: i1, ret, cone na ya
  - item: i2, blu, sphere na ya
  - item: i4, ret, cube na ya
  - item: i6, blu, cube na no
  - item: i7, ret, cone na no
  - item: i3, ret, cube na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, color blue
  - predict yes if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, color màxke
  - theory2 theory size 4
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, màxke, sphere no
  - item: i5, blue, cone no
  - item: i1, màxke, cone yes
  - item: i2, blue, sphere yes
  - item: i4, màxke, cube yes
  - item: i6, blue, cube no
  - item: i7, màxke, cone no
  - item: i3, màxke, cube yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - predict yes if: theory2, бос а баьццара
  - predict yes if: theory2, shape а cube
  - predict yes if: theory0, бос а blue
  - predict yes if: theory3, бос а blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, бос а цӏе
  - theory2 theory size 4
  - predict yes if: theory1, shape а cone
  - predict yes if: theory1, shape а cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, цӏе, sphere а no
  - item: i5, blue, cone а no
  - item: i1, цӏе, cone а yes
  - item: i2, blue, sphere а yes
  - item: i4, цӏе, cube а yes
  - item: i6, blue, cube а no
  - item: i7, цӏе, cone а no
  - item: i3, цӏе, cube а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - 예언하다 네 만약: theory2, 색깔 녹색의
  - 예언하다 네 만약: theory2, 모양 정육면체
  - 예언하다 네 만약: theory0, 색깔 푸르다
  - 예언하다 네 만약: theory3, 색깔 푸르다
  - theory3 4 이론 크기
  - theory0 2 이론 크기
  - 예언하다 네 만약: theory3, 색깔 빨갛다
  - theory2 4 이론 크기
  - 예언하다 네 만약: theory1, 모양 솔방울
  - 예언하다 네 만약: theory1, 모양 정육면체
  - theory1 4 이론 크기
cost model: 예외 cost 3.
자료:
  - 아이템: i0, 빨갛다, 구체 아니요
  - 아이템: i5, 푸르다, 솔방울 아니요
  - 아이템: i1, 빨갛다, 솔방울 네
  - 아이템: i2, 푸르다, 구체 네
  - 아이템: i4, 빨갛다, 정육면체 네
  - 아이템: i6, 푸르다, 정육면체 아니요
  - 아이템: i7, 빨갛다, 솔방울 아니요
  - 아이템: i3, 빨갛다, 정육면체 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - aurresan bai if: theory2, kolore eta berde
  - aurresan bai if: theory2, shape eta kubo
  - aurresan bai if: theory0, kolore eta urdin
  - aurresan bai if: theory3, kolore eta urdin
  - theory3 4 theory tamaina
  - theory0 2 theory tamaina
  - aurresan bai if: theory3, kolore eta gorri
  - theory2 4 theory tamaina
  - aurresan bai if: theory1, shape eta pinaburu
  - aurresan bai if: theory1, shape eta kubo
  - theory1 4 theory tamaina
Cost model: exception kostatu 3.
Data:
  - item: i0, gorri, esfera eta no
  - item: i5, urdin, pinaburu eta no
  - item: i1, gorri, pinaburu eta bai
  - item: i2, urdin, esfera eta bai
  - item: i4, gorri, kubo eta bai
  - item: i6, urdin, kubo eta no
  - item: i7, gorri, pinaburu eta no
  - item: i3, gorri, kubo eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory2, shape ᎠᎴ cube
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - theory3 theory size 4
  - theory0 theory size 2
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - theory2 theory size 4
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, ᎩᎦᎨ, sphere ᎠᎴ ᏝᏃ
  - item: i5, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᏝᏃ
  - item: i1, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
  - item: i2, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᎥᎥ
  - item: i4, ᎩᎦᎨ, cube ᎠᎴ ᎥᎥ
  - item: i6, ᏌᎪᏂᎨᎢ, cube ᎠᎴ ᏝᏃ
  - item: i7, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i3, ᎩᎦᎨ, cube ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict arí if: theory2, llimphi waylla
  - predict arí if: theory2, shape cube
  - predict arí if: theory0, llimphi anqas
  - predict arí if: theory3, llimphi anqas
  - theory3 theory chhika 4
  - theory0 theory chhika 2
  - predict arí if: theory3, llimphi puka
  - theory2 theory chhika 4
  - predict arí if: theory1, shape cone
  - predict arí if: theory1, shape cube
  - theory1 theory chhika 4
Cost model: exception cost 3.
Data:
  - item: i0, puka, sphere mánan
  - item: i5, anqas, cone mánan
  - item: i1, puka, cone arí
  - item: i2, anqas, sphere arí
  - item: i4, puka, cube arí
  - item: i6, anqas, cube mánan
  - item: i7, puka, cone mánan
  - item: i3, puka, cube arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color ыкӏи уцышъо
  - predict yes if: theory2, shape ыкӏи cube
  - predict yes if: theory0, color ыкӏи blue
  - predict yes if: theory3, color ыкӏи blue
  - theory3 4 тиориэ size
  - theory0 2 тиориэ size
  - predict yes if: theory3, color ыкӏи плъыжьы
  - theory2 4 тиориэ size
  - predict yes if: theory1, shape ыкӏи cone
  - predict yes if: theory1, shape ыкӏи cube
  - theory1 4 тиориэ size
Cost model: exception cost 3.
Data:
  - item: i0, плъыжьы, sphere ыкӏи no
  - item: i5, blue, cone ыкӏи no
  - item: i1, плъыжьы, cone ыкӏи yes
  - item: i2, blue, sphere ыкӏи yes
  - item: i4, плъыжьы, cube ыкӏи yes
  - item: i6, blue, cube ыкӏи no
  - item: i7, плъыжьы, cone ыкӏи no
  - item: i3, плъыжьы, cube ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - predict yes if: theory2, xim thiab ntsuab
  - predict yes if: theory2, shape thiab cube
  - predict yes if: theory0, xim thiab xiav
  - predict yes if: theory3, xim thiab xiav
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, xim thiab liab
  - theory2 theory size 4
  - predict yes if: theory1, shape thiab cone
  - predict yes if: theory1, shape thiab cube
  - theory1 theory size 4
Cost model: exception nqi 3.
Data:
  - item: i0, liab, sphere thiab no
  - item: i5, xiav, cone thiab no
  - item: i1, liab, cone thiab yes
  - item: i2, xiav, sphere thiab yes
  - item: i4, liab, cube thiab yes
  - item: i6, xiav, cube thiab no
  - item: i7, liab, cone thiab no
  - item: i3, liab, cube thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - predict ngai if: theory2, color green
  - predict ngai if: theory2, shape cube
  - predict ngai if: theory0, color blue
  - predict ngai if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict ngai if: theory3, color red
  - theory2 theory size 4
  - predict ngai if: theory1, shape cone
  - predict ngai if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere woi
  - item: i5, blue, cone woi
  - item: i1, red, cone ngai
  - item: i2, blue, sphere ngai
  - item: i4, red, cube ngai
  - item: i6, blue, cube woi
  - item: i7, red, cone woi
  - item: i3, red, cube ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - predict i̠i if: theory2, color micha' green
  - predict i̠i if: theory2, shape micha' cube
  - predict i̠i if: theory0, color micha' blue
  - predict i̠i if: theory3, color micha' blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict i̠i if: theory3, color micha' homa
  - theory2 theory size 4
  - predict i̠i if: theory1, shape micha' cone
  - predict i̠i if: theory1, shape micha' cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, homa, sphere micha' no
  - item: i5, blue, cone micha' no
  - item: i1, homa, cone micha' i̠i
  - item: i2, blue, sphere micha' i̠i
  - item: i4, homa, cube micha' i̠i
  - item: i6, blue, cube micha' no
  - item: i7, homa, cone micha' no
  - item: i3, homa, cube micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - predict yes if: theory2, color カ green
  - predict yes if: theory2, shape カ cube
  - predict yes if: theory0, color カ blue
  - predict yes if: theory3, color カ blue
  - theory3 4 theory size
  - theory0 2 theory size
  - predict yes if: theory3, color カ フレ
  - theory2 4 theory size
  - predict yes if: theory1, shape カ cone
  - predict yes if: theory1, shape カ cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, フレ, sphere カ ソモ
  - item: i5, blue, cone カ ソモ
  - item: i1, フレ, cone カ yes
  - item: i2, blue, sphere カ yes
  - item: i4, フレ, cube カ yes
  - item: i6, blue, cube カ ソモ
  - item: i7, フレ, cone カ ソモ
  - item: i3, フレ, cube カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes эвыр: theory2, пэран ынкъам green
  - predict yes эвыр: theory2, shape ынкъам cube
  - predict yes эвыр: theory0, пэран ынкъам ныԓиԓиԓӄин
  - predict yes эвыр: theory3, пэран ынкъам ныԓиԓиԓӄин
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes эвыр: theory3, пэран ынкъам чеԓгыԓьын
  - theory2 theory size 4
  - predict yes эвыр: theory1, shape ынкъам cone
  - predict yes эвыр: theory1, shape ынкъам cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, чеԓгыԓьын, sphere ынкъам no
  - item: i5, ныԓиԓиԓӄин, cone ынкъам no
  - item: i1, чеԓгыԓьын, cone ынкъам yes
  - item: i2, ныԓиԓиԓӄин, sphere ынкъам yes
  - item: i4, чеԓгыԓьын, cube ынкъам yes
  - item: i6, ныԓиԓиԓӄин, cube ынкъам no
  - item: i7, чеԓгыԓьын, cone ынкъам no
  - item: i3, чеԓгыԓьын, cube ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes wa: theory2, color yéetel yaʼax
  - predict yes wa: theory2, shape yéetel cube
  - predict yes wa: theory0, color yéetel blue
  - predict yes wa: theory3, color yéetel blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes wa: theory3, color yéetel chak
  - theory2 theory size 4
  - predict yes wa: theory1, shape yéetel cone
  - predict yes wa: theory1, shape yéetel cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, chak, sphere yéetel no
  - item: i5, blue, cone yéetel no
  - item: i1, chak, cone yéetel yes
  - item: i2, blue, sphere yéetel yes
  - item: i4, chak, cube yéetel yes
  - item: i6, blue, cube yéetel no
  - item: i7, chak, cone yéetel no
  - item: i3, chak, cube yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict hą if: theory2, color green
  - predict hą if: theory2, shape cube
  - predict hą if: theory0, color blue
  - predict hą if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict hą if: theory3, color red
  - theory2 theory size 4
  - predict hą if: theory1, shape cone
  - predict hą if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blue, cone no
  - item: i1, red, cone hą
  - item: i2, blue, sphere hą
  - item: i4, red, cube hą
  - item: i6, blue, cube no
  - item: i7, red, cone no
  - item: i3, red, cube hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - predict yes if: theory2, color na green
  - predict yes if: theory2, shape na cube
  - predict yes if: theory0, color na blue
  - predict yes if: theory3, color na blue
  - theory3 4 theory size
  - theory0 2 theory size
  - predict yes if: theory3, color na red
  - theory2 4 theory size
  - predict yes if: theory1, shape na cone
  - predict yes if: theory1, shape na cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere na no
  - item: i5, blue, cone na no
  - item: i1, red, cone na yes
  - item: i2, blue, sphere na yes
  - item: i4, red, cube na yes
  - item: i6, blue, cube na no
  - item: i7, red, cone na no
  - item: i3, red, cube na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict heẽ if: theory2, color green
  - predict heẽ if: theory2, shape cube
  - predict heẽ if: theory0, color blue
  - predict heẽ if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict heẽ if: theory3, color pytã
  - theory2 theory size 4
  - predict heẽ if: theory1, shape cone
  - predict heẽ if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, pytã, sphere nahániri
  - item: i5, blue, cone nahániri
  - item: i1, pytã, cone heẽ
  - item: i2, blue, sphere heẽ
  - item: i4, pytã, cube heẽ
  - item: i6, blue, cube nahániri
  - item: i7, pytã, cone nahániri
  - item: i3, pytã, cube heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict jisa if: theory2, sami ch’uxña
  - predict jisa if: theory2, shape cube
  - predict jisa if: theory0, sami larama
  - predict jisa if: theory3, sami larama
  - theory3 4 theory size
  - theory0 2 theory size
  - predict jisa if: theory3, sami chupika
  - theory2 4 theory size
  - predict jisa if: theory1, shape cone
  - predict jisa if: theory1, shape cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, chupika, sphere no
  - item: i5, larama, cone no
  - item: i1, chupika, cone jisa
  - item: i2, larama, sphere jisa
  - item: i4, chupika, cube jisa
  - item: i6, larama, cube no
  - item: i7, chupika, cone no
  - item: i3, chupika, cube jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ʻée if: theory2, color ǂʻá green
  - predict ʻée if: theory2, shape ǂʻá cube
  - predict ʻée if: theory0, color ǂʻá blue
  - predict ʻée if: theory3, color ǂʻá blue
  - theory3 theory ʻǁnàhã 4
  - theory0 theory ʻǁnàhã 2
  - predict ʻée if: theory3, color ǂʻá red
  - theory2 theory ʻǁnàhã 4
  - predict ʻée if: theory1, shape ǂʻá cone
  - predict ʻée if: theory1, shape ǂʻá cube
  - theory1 theory ʻǁnàhã 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere ǂʻá ʻĩĩ
  - item: i5, blue, cone ǂʻá ʻĩĩ
  - item: i1, red, cone ǂʻá ʻée
  - item: i2, blue, sphere ǂʻá ʻée
  - item: i4, red, cube ǂʻá ʻée
  - item: i6, blue, cube ǂʻá ʻĩĩ
  - item: i7, red, cone ǂʻá ʻĩĩ
  - item: i3, red, cube ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, вал green
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, вал blue
  - predict yes if: theory3, вал blue
  - theory3 4 theory size
  - theory0 2 theory size
  - predict yes if: theory3, вал red
  - theory2 4 theory size
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blue, cone no
  - item: i1, red, cone yes
  - item: i2, blue, sphere yes
  - item: i4, red, cube yes
  - item: i6, blue, cube no
  - item: i7, red, cone no
  - item: i3, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, color blue
  - predict yes if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, color red
  - theory2 theory size 4
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blue, cone no
  - item: i1, red, cone yes
  - item: i2, blue, sphere yes
  - item: i4, red, cube yes
  - item: i6, blue, cube no
  - item: i7, red, cone no
  - item: i3, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, color blue
  - predict yes if: theory3, color blue
  - theory3 4 theory size
  - theory0 2 theory size
  - predict yes if: theory3, color red
  - theory2 4 theory size
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blue, cone no
  - item: i1, red, cone yes
  - item: i2, blue, sphere yes
  - item: i4, red, cube yes
  - item: i6, blue, cube no
  - item: i7, red, cone no
  - item: i3, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ee if: theory2, color green
  - predict ee if: theory2, shape cube
  - predict ee if: theory0, color blue
  - predict ee if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict ee if: theory3, color red
  - theory2 theory size 4
  - predict ee if: theory1, shape cone
  - predict ee if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blue, cone no
  - item: i1, red, cone ee
  - item: i2, blue, sphere ee
  - item: i4, red, cube ee
  - item: i6, blue, cube no
  - item: i7, red, cone no
  - item: i3, red, cube ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, color blue
  - predict yes if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, color red
  - theory2 theory size 4
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere inte
  - item: i5, blue, cone inte
  - item: i1, red, cone yes
  - item: i2, blue, sphere yes
  - item: i4, red, cube yes
  - item: i6, blue, cube inte
  - item: i7, red, cone inte
  - item: i3, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - prédire oui si: theory2, couleur et vert
  - prédire oui si: theory2, forme et cube
  - prédire oui si: theory0, couleur et bleu
  - prédire oui si: theory3, couleur et bleu
  - theory3 théorie taille 4
  - theory0 théorie taille 2
  - prédire oui si: theory3, couleur et rouge
  - theory2 théorie taille 4
  - prédire oui si: theory1, forme et cône
  - prédire oui si: theory1, forme et cube
  - theory1 théorie taille 4
Coûts : exception coûter 3.
Les données sont:
  - article: i0, rouge, sphère et no
  - article: i5, bleu, cône et no
  - article: i1, rouge, cône et oui
  - article: i2, bleu, sphère et oui
  - article: i4, rouge, cube et oui
  - article: i6, bleu, cube et no
  - article: i7, rouge, cône et no
  - article: i3, rouge, cube et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - predict jes se: theory2, koloro kaj verda
  - predict jes se: theory2, formi kaj kubo
  - predict jes se: theory0, koloro kaj blua
  - predict jes se: theory3, koloro kaj blua
  - theory3 teorio grandeco 4
  - theory0 teorio grandeco 2
  - predict jes se: theory3, koloro kaj ruĝa
  - theory2 teorio grandeco 4
  - predict jes se: theory1, formi kaj strobilo
  - predict jes se: theory1, formi kaj kubo
  - theory1 teorio grandeco 4
Kosto pozistino: escepto kosti 3.
Datumo:
  - ero: i0, ruĝa, sfero kaj no
  - ero: i5, blua, strobilo kaj no
  - ero: i1, ruĝa, strobilo kaj jes
  - ero: i2, blua, sfero kaj jes
  - ero: i4, ruĝa, kubo kaj jes
  - ero: i6, blua, kubo kaj no
  - ero: i7, ruĝa, strobilo kaj no
  - ero: i3, ruĝa, kubo kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - megjósol igen ha: theory2, szín és zöld
  - megjósol igen ha: theory2, alak és kocka
  - megjósol igen ha: theory0, szín és kék
  - megjósol igen ha: theory3, szín és kék
  - theory3 elmélet méret 4
  - theory0 elmélet méret 2
  - megjósol igen ha: theory3, szín és piros
  - theory2 elmélet méret 4
  - megjósol igen ha: theory1, alak és toboz
  - megjósol igen ha: theory1, alak és kocka
  - theory1 elmélet méret 4
Költség manöken: kivétel kerül 3.
Adat:
  - cikk: i0, piros, gömb és no
  - cikk: i5, kék, toboz és no
  - cikk: i1, piros, toboz és igen
  - cikk: i2, kék, gömb és igen
  - cikk: i4, piros, kocka és igen
  - cikk: i6, kék, kocka és no
  - cikk: i7, piros, toboz és no
  - cikk: i3, piros, kocka és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - predict係要是:theory2, 色及綠色嘅
  - predict係要是:theory2, 形狀及cube
  - predict係要是:theory0, 色及藍色嘅
  - predict係要是:theory3, 色及藍色嘅
  - theory3theory size4
  - theory0theory size2
  - predict係要是:theory3, 色及紅
  - theory2theory size4
  - predict係要是:theory1, 形狀及cone
  - predict係要是:theory1, 形狀及cube
  - theory1theory size4
cost model:exception cost:3.
數據:
  - item:i0, 紅, sphere及no
  - item:i5, 藍色嘅, cone及no
  - item:i1, 紅, cone及係
  - item:i2, 藍色嘅, sphere及係
  - item:i4, 紅, cube及係
  - item:i6, 藍色嘅, cube及no
  - item:i7, 紅, cone及no
  - item:i3, 紅, cube及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - predict yes if: theory2, color とぅ green
  - predict yes if: theory2, shape とぅ cube
  - predict yes if: theory0, color とぅ blue
  - predict yes if: theory3, color とぅ blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, color とぅ red
  - theory2 theory size 4
  - predict yes if: theory1, shape とぅ cone
  - predict yes if: theory1, shape とぅ cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere とぅ no
  - item: i5, blue, cone とぅ no
  - item: i1, red, cone とぅ yes
  - item: i2, blue, sphere とぅ yes
  - item: i4, red, cube とぅ yes
  - item: i6, blue, cube とぅ no
  - item: i7, red, cone とぅ no
  - item: i3, red, cube とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - predict иә егер: theory2, түс және жасыл
  - predict иә егер: theory2, нысан және куб
  - predict иә егер: theory0, түс және көк
  - predict иә егер: theory3, түс және көк
  - theory3 теория мөлшер 4
  - theory0 теория мөлшер 2
  - predict иә егер: theory3, түс және қызыл
  - theory2 теория мөлшер 4
  - predict иә егер: theory1, нысан және бүршік
  - predict иә егер: theory1, нысан және куб
  - theory1 теория мөлшер 4
Тұру үлгі: ерекшелік тұру 3.
Деректер:
  - зат: i0, қызыл, сфера және жоқ
  - зат: i5, көк, бүршік және жоқ
  - зат: i1, қызыл, бүршік және иә
  - зат: i2, көк, сфера және иә
  - зат: i4, қызыл, куб және иә
  - зат: i6, көк, куб және жоқ
  - зат: i7, қызыл, бүршік және жоқ
  - зат: i3, қызыл, куб және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - predict כֵּן אִם: theory2, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory2, צוּרָה וְ־ קובייה
  - predict כֵּן אִם: theory0, צֶבַע וְ־ כָּחוֹל
  - predict כֵּן אִם: theory3, צֶבַע וְ־ כָּחוֹל
  - theory3 תּוֹרָה גֹּדֶל 4
  - theory0 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory3, צֶבַע וְ־ אָדוֹם
  - theory2 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory1, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory1, צוּרָה וְ־ קובייה
  - theory1 תּוֹרָה גֹּדֶל 4
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 3.
נְתוּנִים:
  - אבזר: i0, אָדוֹם, כַּדּוּר וְ־ no
  - אבזר: i5, כָּחוֹל, אצטרובל וְ־ no
  - אבזר: i1, אָדוֹם, אצטרובל וְ־ כֵּן
  - אבזר: i2, כָּחוֹל, כַּדּוּר וְ־ כֵּן
  - אבזר: i4, אָדוֹם, קובייה וְ־ כֵּן
  - אבזר: i6, כָּחוֹל, קובייה וְ־ no
  - אבזר: i7, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i3, אָדוֹם, קובייה וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - predict იახ if: theory2, color დო green
  - predict იახ if: theory2, shape დო cube
  - predict იახ if: theory0, color დო blue
  - predict იახ if: theory3, color დო blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict იახ if: theory3, color დო red
  - theory2 theory size 4
  - predict იახ if: theory1, shape დო cone
  - predict იახ if: theory1, shape დო cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere დო no
  - item: i5, blue, cone დო no
  - item: i1, red, cone დო იახ
  - item: i2, blue, sphere დო იახ
  - item: i4, red, cube დო იახ
  - item: i6, blue, cube დო no
  - item: i7, red, cone დო no
  - item: i3, red, cube დო იახ
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - predict ya jika: theory2, warna dan hijau
  - predict ya jika: theory2, bentuk dan kubus
  - predict ya jika: theory0, warna dan biru
  - predict ya jika: theory3, warna dan biru
  - theory3 teori ukuran 4
  - theory0 teori ukuran 2
  - predict ya jika: theory3, warna dan merah
  - theory2 teori ukuran 4
  - predict ya jika: theory1, bentuk dan kerucut
  - predict ya jika: theory1, bentuk dan kubus
  - theory1 teori ukuran 4
Cost model: pengecualian menghabiskan biaya 3.
Data:
  - benda: i0, merah, bola dan no
  - benda: i5, biru, kerucut dan no
  - benda: i1, merah, kerucut dan ya
  - benda: i2, biru, bola dan ya
  - benda: i4, merah, kubus dan ya
  - benda: i6, biru, kubus dan no
  - benda: i7, merah, kerucut dan no
  - benda: i3, merah, kubus dan ya
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory2, គ្រោងនឹងគូប
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងខៀវ
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងខៀវ
  - theory3ទ្រឹស្ដីទំហំ4
  - theory0ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងក្រហម
  - theory2ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងកោណ
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងគូប
  - theory1ទ្រឹស្ដីទំហំ4
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:3.
ទិន្នន័យ:
  - item:i0, ក្រហម, គោលនឹងទេ
  - item:i5, ខៀវ, កោណនឹងទេ
  - item:i1, ក្រហម, កោណនឹងច៎ា
  - item:i2, ខៀវ, គោលនឹងច៎ា
  - item:i4, ក្រហម, គូបនឹងច៎ា
  - item:i6, ខៀវ, គូបនឹងទេ
  - item:i7, ក្រហម, កោណនឹងទេ
  - item:i3, ក្រហម, គូបនឹងច៎ា
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ຮູບຮ່າງແລະລູກບາດ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຟ້າ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະຟ້າ
  - theory3ທິດສະດີຂະຫນາດ4
  - theory0ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະແດງ
  - theory2ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະcone
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະລູກບາດ
  - theory1ທິດສະດີຂະຫນາດ4
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:3.
ຂໍ້ມູນ:
  - item:i0, ແດງ, ຄະດີແລະno
  - item:i5, ຟ້າ, coneແລະno
  - item:i1, ແດງ, coneແລະແມ່ນ
  - item:i2, ຟ້າ, ຄະດີແລະແມ່ນ
  - item:i4, ແດງ, ລູກບາດແລະແມ່ນ
  - item:i6, ຟ້າ, ລູກບາດແລະno
  - item:i7, ແດງ, coneແລະno
  - item:i3, ແດງ, ລູກບາດແລະແມ່ນ
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - predict అవును అయితే: theory2, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory2, ఆకారం మరియు cube
  - predict అవును అయితే: theory0, రంగు మరియు నీలము
  - predict అవును అయితే: theory3, రంగు మరియు నీలము
  - theory3 4 theory size
  - theory0 2 theory size
  - predict అవును అయితే: theory3, రంగు మరియు ఎర్ర
  - theory2 4 theory size
  - predict అవును అయితే: theory1, ఆకారం మరియు cone
  - predict అవును అయితే: theory1, ఆకారం మరియు cube
  - theory1 4 theory size
cost model: exception cost 3.
data:
  - item: i0, ఎర్ర, గోళము మరియు no
  - item: i5, నీలము, cone మరియు no
  - item: i1, ఎర్ర, cone మరియు అవును
  - item: i2, నీలము, గోళము మరియు అవును
  - item: i4, ఎర్ర, cube మరియు అవును
  - item: i6, నీలము, cube మరియు no
  - item: i7, ఎర్ర, cone మరియు no
  - item: i3, ఎర్ర, cube మరియు అవును
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - predict hẹn if: theory2, color green
  - predict hẹn if: theory2, shape cube
  - predict hẹn if: theory0, color dúdú
  - predict hẹn if: theory3, color dúdú
  - theory3 theory size 4
  - theory0 theory size 2
  - predict hẹn if: theory3, color pupa
  - theory2 theory size 4
  - predict hẹn if: theory1, shape cone
  - predict hẹn if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, pupa, sphere rárá
  - item: i5, dúdú, cone rárá
  - item: i1, pupa, cone bẹ́ẹ̀ ni
  - item: i2, dúdú, sphere bẹ́ẹ̀ ni
  - item: i4, pupa, cube bẹ́ẹ̀ ni
  - item: i6, dúdú, cube rárá
  - item: i7, pupa, cone rárá
  - item: i3, pupa, cube bẹ́ẹ̀ ni
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict au if: theory2, color green
  - predict au if: theory2, shape cube
  - predict au if: theory0, color blue
  - predict au if: theory3, color blue
  - theory3 4 theory size
  - theory0 2 theory size
  - predict au if: theory3, color łichíí
  - theory2 4 theory size
  - predict au if: theory1, shape cone
  - predict au if: theory1, shape cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, łichíí, sphere no
  - item: i5, blue, cone no
  - item: i1, łichíí, cone au
  - item: i2, blue, sphere au
  - item: i4, łichíí, cube au
  - item: i6, blue, cube no
  - item: i7, łichíí, cone no
  - item: i3, łichíí, cube au
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - predict yes if: theory2, color green
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, color blue
  - predict yes if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, color red
  - theory2 theory size 4
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 theory size 4
cost model: exception cost 3.
data:
  - item: i0, red, sphere ᐋᒃᑲ
  - item: i5, blue, cone ᐋᒃᑲ
  - item: i1, red, cone yes
  - item: i2, blue, sphere yes
  - item: i4, red, cube yes
  - item: i6, blue, cube ᐋᒃᑲ
  - item: i7, red, cone ᐋᒃᑲ
  - item: i3, red, cube yes
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color grun
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, color blaw
  - predict yes if: theory3, color blaw
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, color red
  - theory2 theory size 4
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blaw, cone no
  - item: i1, red, cone yes
  - item: i2, blaw, sphere yes
  - item: i4, red, cube yes
  - item: i6, blaw, cube no
  - item: i7, red, cone no
  - item: i3, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, color blue
  - predict yes if: theory3, color blue
  - theory3 theory size 4
  - theory0 theory size 2
  - predict yes if: theory3, color red
  - theory2 theory size 4
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 theory size 4
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blue, cone no
  - item: i1, red, cone yes
  - item: i2, blue, sphere yes
  - item: i4, red, cube yes
  - item: i6, blue, cube no
  - item: i7, red, cone no
  - item: i3, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, ранг къацу
  - predict yes if: theory2, shape cube
  - predict yes if: theory0, ранг blue
  - predict yes if: theory3, ранг blue
  - theory3 4 theory size
  - theory0 2 theory size
  - predict yes if: theory3, ранг red
  - theory2 4 theory size
  - predict yes if: theory1, shape cone
  - predict yes if: theory1, shape cube
  - theory1 4 theory size
Cost model: exception cost 3.
Data:
  - item: i0, red, sphere no
  - item: i5, blue, cone no
  - item: i1, red, cone yes
  - item: i2, blue, sphere yes
  - item: i4, red, cube yes
  - item: i6, blue, cube no
  - item: i7, red, cone no
  - item: i3, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - theory0 theory size 2
  - theory1 theory size 4
  - predicts yes if: theory1, colour and blue
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, shape and sphere
  - predicts yes if: theory1, shape and cone
  - predicts yes if: theory0, colour and blue
  - theory2 theory size 2
  - theory3 theory size 2
Costs: exception cost 3.
The data are:
  - item: i3, green, cube and yes
  - item: i2, red, cube and no
  - item: i6, blue, cube and yes
  - item: i4, blue, sphere and no
  - item: i1, green, cone and yes
  - item: i5, red, sphere and yes
  - item: i0, red, cone and yes
  - item: i7, red, sphere and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - theory0 el tamaño de la teoría 2
  - theory1 el tamaño de la teoría 4
  - predice sí si: theory1, es de color y azul
  - predice sí si: theory3, forma y esfera
  - predice sí si: theory2, forma y esfera
  - predice sí si: theory1, forma y cono
  - predice sí si: theory0, es de color y azul
  - theory2 el tamaño de la teoría 2
  - theory3 el tamaño de la teoría 2
Costes: exception el coste: 3.
Los datos son:
  - el elemento: i3, verde, cubo y sí
  - el elemento: i2, rojo, cubo y no
  - el elemento: i6, azul, cubo y sí
  - el elemento: i4, azul, esfera y no
  - el elemento: i1, verde, cono y sí
  - el elemento: i5, rojo, esfera y sí
  - el elemento: i0, rojo, cono y sí
  - el elemento: i7, rojo, esfera y no
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - theory0的理论规模是2
  - theory1的理论规模是4
  - 预测是若：theory1、颜色和蓝色的
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、形状和球体
  - 预测是若：theory1、形状和圆锥
  - 预测是若：theory0、颜色和蓝色的
  - theory2的理论规模是2
  - theory3的理论规模是2
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i3、绿色的、立方体和是
  - 的物品是：i2、红色的、立方体和否
  - 的物品是：i6、蓝色的、立方体和是
  - 的物品是：i4、蓝色的、球体和否
  - 的物品是：i1、绿色的、圆锥和是
  - 的物品是：i5、红色的、球体和是
  - 的物品是：i0、红色的、圆锥和是
  - 的物品是：i7、红色的、球体和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - theory0 2 kuram boyut
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory1, renk ve mavi
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, şekil ve küre
  - öngörür evet eğer: theory1, şekil ve koni
  - öngörür evet eğer: theory0, renk ve mavi
  - theory2 2 kuram boyut
  - theory3 2 kuram boyut
Cost model: exception maliyet: 3.
Veriler:
  - item: i3, yeşil, küp ve evet
  - item: i2, kırmızı, küp ve hayır
  - item: i6, mavi, küp ve evet
  - item: i4, mavi, küre ve hayır
  - item: i1, yeşil, koni ve evet
  - item: i5, kırmızı, küre ve evet
  - item: i0, kırmızı, koni ve evet
  - item: i7, kırmızı, küre ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict ndiyo kama: theory1, rangi na buluu
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, umbo na tufe
  - predict ndiyo kama: theory1, umbo na koni
  - predict ndiyo kama: theory0, rangi na buluu
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception gharimu: 3.
Data:
  - item: i3, kijani, mchemraba na ndiyo
  - item: i2, ekundu, mchemraba na hapana
  - item: i6, buluu, mchemraba na ndiyo
  - item: i4, buluu, tufe na hapana
  - item: i1, kijani, koni na ndiyo
  - item: i5, ekundu, tufe na ndiyo
  - item: i0, ekundu, koni na ndiyo
  - item: i7, ekundu, tufe na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - theory0 theory size 2
  - theory1 theory size 4
  - predicts yes if: theory1, colour and blue
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, shape and sphere
  - predicts yes if: theory1, shape and cone
  - predicts yes if: theory0, colour and blue
  - theory2 theory size 2
  - theory3 theory size 2
Costs: exception cost 3.
The data are:
  - item: i3, green, cube and yes
  - item: i2, red, cube and no
  - item: i6, blue, cube and yes
  - item: i4, blue, sphere and no
  - item: i1, green, cone and yes
  - item: i5, red, sphere and yes
  - item: i0, red, cone and yes
  - item: i7, red, sphere and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(theory_size theory0 2) (theory_size theory1 4) (predicts_yes_if theory1 color blue) (predicts_yes_if theory3 shape sphere) (predicts_yes_if theory2 shape sphere) (predicts_yes_if theory1 shape cone) (predicts_yes_if theory0 color blue) (theory_size theory2 2) (theory_size theory3 2)], cost_model: [(exception_cost 3)], data: [(item i3 green cube yes) (item i2 red cube no) (item i6 blue cube yes) (item i4 blue sphere no) (item i1 green cone yes) (item i5 red sphere yes) (item i0 red cone yes) (item i7 red sphere no)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - theory0 Theorie Größe 2
  - theory1 Theorie Größe 4
  - vorhersagen ja wenn: theory1, Farbe und blau
  - vorhersagen ja wenn: theory3, Form und Kugel
  - vorhersagen ja wenn: theory2, Form und Kugel
  - vorhersagen ja wenn: theory1, Form und Kegel
  - vorhersagen ja wenn: theory0, Farbe und blau
  - theory2 Theorie Größe 2
  - theory3 Theorie Größe 2
Kosten: Ausnahme kosten 3.
Die Daten sind:
  - Ding: i3, grün, Kubus und ja
  - Ding: i2, rot, Kubus und nein
  - Ding: i6, blau, Kubus und ja
  - Ding: i4, blau, Kugel und nein
  - Ding: i1, grün, Kegel und ja
  - Ding: i5, rot, Kugel und ja
  - Ding: i0, rot, Kegel und ja
  - Ding: i7, rot, Kugel und nein
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - theory0 тѐо̄рија величѝна 2
  - theory1 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory1, бо̀ја и пла̑в
  - predvidjeti да ȁko: theory3, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory2, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory1, о̏блӣк и stožac
  - predvidjeti да ȁko: theory0, бо̀ја и пла̑в
  - theory2 тѐо̄рија величѝна 2
  - theory3 тѐо̄рија величѝна 2
Trošak мо̀дел: изузе́так ко̏штати 3.
Податци:
  - item: i3, зѐлен, коцка и да
  - item: i2, цр̀вен, коцка и no
  - item: i6, пла̑в, коцка и да
  - item: i4, пла̑в, сфе́ра и no
  - item: i1, зѐлен, stožac и да
  - item: i5, цр̀вен, сфе́ра и да
  - item: i0, цр̀вен, stožac и да
  - item: i7, цр̀вен, сфе́ра и no
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - theory0 teoria koko 2
  - theory1 teoria koko 4
  - ennakoida kyllä jos: theory1, väri ja sininen
  - ennakoida kyllä jos: theory3, muoto ja pallo
  - ennakoida kyllä jos: theory2, muoto ja pallo
  - ennakoida kyllä jos: theory1, muoto ja kartio
  - ennakoida kyllä jos: theory0, väri ja sininen
  - theory2 teoria koko 2
  - theory3 teoria koko 2
Kustannus malli: poikkeus maksaa 3.
Tieto:
  - esine: i3, vihreä, kuutio ja kyllä
  - esine: i2, punainen, kuutio ja ei
  - esine: i6, sininen, kuutio ja kyllä
  - esine: i4, sininen, pallo ja ei
  - esine: i1, vihreä, kartio ja kyllä
  - esine: i5, punainen, pallo ja kyllä
  - esine: i0, punainen, kartio ja kyllä
  - esine: i7, punainen, pallo ja ei
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - theory0的理论规模是2
  - theory1的理论规模是4
  - 预测是若：theory1、颜色和蓝色的
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、形状和球体
  - 预测是若：theory1、形状和圆锥
  - 预测是若：theory0、颜色和蓝色的
  - theory2的理论规模是2
  - theory3的理论规模是2
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i3、绿色的、立方体和是
  - 的物品是：i2、红色的、立方体和否
  - 的物品是：i6、蓝色的、立方体和是
  - 的物品是：i4、蓝色的、球体和否
  - 的物品是：i1、绿色的、圆锥和是
  - 的物品是：i5、红色的、球体和是
  - 的物品是：i0、红色的、圆锥和是
  - 的物品是：i7、红色的、球体和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - theory02理論大きさ
  - theory14理論大きさ
  - 予言するはいもし...:theory1, 色と青い
  - 予言するはいもし...:theory3, 形と球
  - 予言するはいもし...:theory2, 形と球
  - 予言するはいもし...:theory1, 形と松毬
  - 予言するはいもし...:theory0, 色と青い
  - theory22理論大きさ
  - theory32理論大きさ
費用モデル:例外費やす:3.
資料:
  - 項目:i3, 緑の, 立方体とはい
  - 項目:i2, 赤い, 立方体といいえ
  - 項目:i6, 青い, 立方体とはい
  - 項目:i4, 青い, 球といいえ
  - 項目:i1, 緑の, 松毬とはい
  - 項目:i5, 赤い, 球とはい
  - 項目:i0, 赤い, 松毬とはい
  - 項目:i7, 赤い, 球といいえ
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - theory0 2 kuram boyut
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory1, renk ve mavi
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, şekil ve küre
  - öngörür evet eğer: theory1, şekil ve koni
  - öngörür evet eğer: theory0, renk ve mavi
  - theory2 2 kuram boyut
  - theory3 2 kuram boyut
Cost model: exception maliyet: 3.
Veriler:
  - item: i3, yeşil, küp ve evet
  - item: i2, kırmızı, küp ve hayır
  - item: i6, mavi, küp ve evet
  - item: i4, mavi, küre ve hayır
  - item: i1, yeşil, koni ve evet
  - item: i5, kırmızı, küre ve evet
  - item: i0, kırmızı, koni ve evet
  - item: i7, kırmızı, küre ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - نَظَرِيَّة مَقَاس theory0 2
  - نَظَرِيَّة مَقَاس theory1 4
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَزْرَق
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَزْرَق
  - نَظَرِيَّة مَقَاس theory2 2
  - نَظَرِيَّة مَقَاس theory3 2
cost model: اِسْتِثْنَاء cost 3.
بَيَانَات:
  - بَنْد: i3، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i2، أَحْمَر، كَعْبَة وَـ no
  - بَنْد: i6، أَزْرَق، كَعْبَة وَـ نَعَم
  - بَنْد: i4، أَزْرَق، كُرَة وَـ no
  - بَنْد: i1، أَخْضَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i5، أَحْمَر، كُرَة وَـ نَعَم
  - بَنْد: i0، أَحْمَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i7، أَحْمَر، كُرَة وَـ no
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - theory0 2 თეორია ზომა
  - theory1 4 თეორია ზომა
  - predict დიახ თუ: theory1, ფერი და ლურჯი
  - predict დიახ თუ: theory3, ფორმა და სფერო
  - predict დიახ თუ: theory2, ფორმა და სფერო
  - predict დიახ თუ: theory1, ფორმა და გირჩა
  - predict დიახ თუ: theory0, ფერი და ლურჯი
  - theory2 2 თეორია ზომა
  - theory3 2 თეორია ზომა
Ფასი მოდელი: გამონაკლისი ღირს 3.
Მონაცემები:
  - item: i3, მწვანე, კუბი და დიახ
  - item: i2, წითელი, კუბი და no
  - item: i6, ლურჯი, კუბი და დიახ
  - item: i4, ლურჯი, სფერო და no
  - item: i1, მწვანე, გირჩა და დიახ
  - item: i5, წითელი, სფერო და დიახ
  - item: i0, წითელი, გირჩა და დიახ
  - item: i7, წითელი, სფერო და no
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - ariā size theory0 2
  - ariā size theory1 4
  - matakite āe mēnā: theory1, kano me kahurangi
  - matakite āe mēnā: theory3, āhuahanga me poi
  - matakite āe mēnā: theory2, āhuahanga me poi
  - matakite āe mēnā: theory1, āhuahanga me koeko
  - matakite āe mēnā: theory0, kano me kahurangi
  - ariā size theory2 2
  - ariā size theory3 2
Cost model: hāungatanga cost 3.
Hōtuku:
  - tūemi: i3, kākāriki, mataono me āe
  - tūemi: i2, red, mataono me kāo
  - tūemi: i6, kahurangi, mataono me āe
  - tūemi: i4, kahurangi, poi me kāo
  - tūemi: i1, kākāriki, koeko me āe
  - tūemi: i5, red, poi me āe
  - tūemi: i0, red, koeko me āe
  - tūemi: i7, red, poi me kāo
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - theory0 lý thuyết kích thước 2
  - theory1 lý thuyết kích thước 4
  - dự báo vâng nếu: theory1, màu và blue
  - dự báo vâng nếu: theory3, hình dáng và mặt cầu
  - dự báo vâng nếu: theory2, hình dáng và mặt cầu
  - dự báo vâng nếu: theory1, hình dáng và nón
  - dự báo vâng nếu: theory0, màu và blue
  - theory2 lý thuyết kích thước 2
  - theory3 lý thuyết kích thước 2
Cost model: lệ ngoại cost 3.
Dữ liệu:
  - vật phẩm: i3, green, lập phương và vâng
  - vật phẩm: i2, đỏ, lập phương và no
  - vật phẩm: i6, blue, lập phương và vâng
  - vật phẩm: i4, blue, mặt cầu và no
  - vật phẩm: i1, green, nón và vâng
  - vật phẩm: i5, đỏ, mặt cầu và vâng
  - vật phẩm: i0, đỏ, nón và vâng
  - vật phẩm: i7, đỏ, mặt cầu và no
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - theory0ทฤษฎีขนาด2
  - theory1ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory1, สีและน้ำเงิน
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory0, สีและน้ำเงิน
  - theory2ทฤษฎีขนาด2
  - theory3ทฤษฎีขนาด2
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:3.
ข้อมูล:
  - item:i3, เขียว, ลูกบาศก์และใช่
  - item:i2, แดง, ลูกบาศก์และไฮ้
  - item:i6, น้ำเงิน, ลูกบาศก์และใช่
  - item:i4, น้ำเงิน, ทรงกลมและไฮ้
  - item:i1, เขียว, กรวยและใช่
  - item:i5, แดง, ทรงกลมและใช่
  - item:i0, แดง, กรวยและใช่
  - item:i7, แดง, ทรงกลมและไฮ้
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - theory0 2 theory size
  - theory1 4 theory size
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் நீலம்
  - கணி ஆம் என்றால்: theory3, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory2, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் நீலம்
  - theory2 2 theory size
  - theory3 2 theory size
cost model: exception செலவு 3.
தரவுகள்:
  - item: i3, பச்சை, cube மற்றும் ஆம்
  - item: i2, சிவப்பு, cube மற்றும் no
  - item: i6, நீலம், cube மற்றும் ஆம்
  - item: i4, நீலம், sphere மற்றும் no
  - item: i1, பச்சை, cone மற்றும் ஆம்
  - item: i5, சிவப்பு, sphere மற்றும் ஆம்
  - item: i0, சிவப்பு, cone மற்றும் ஆம்
  - item: i7, சிவப்பு, sphere மற்றும் no
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict ndiyo kama: theory1, rangi na buluu
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, umbo na tufe
  - predict ndiyo kama: theory1, umbo na koni
  - predict ndiyo kama: theory0, rangi na buluu
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception gharimu: 3.
Data:
  - item: i3, kijani, mchemraba na ndiyo
  - item: i2, ekundu, mchemraba na hapana
  - item: i6, buluu, mchemraba na ndiyo
  - item: i4, buluu, tufe na hapana
  - item: i1, kijani, koni na ndiyo
  - item: i5, ekundu, tufe na ndiyo
  - item: i0, ekundu, koni na ndiyo
  - item: i7, ekundu, tufe na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dootłʼizh
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception bą́ą́h ílį́ 3.
Data:
  - item: i3, dinootłʼizh, cube dóó aooʼ
  - item: i2, łichííʼ, cube dóó no
  - item: i6, dootłʼizh, cube dóó aooʼ
  - item: i4, dootłʼizh, sphere dóó no
  - item: i1, dinootłʼizh, cone dóó aooʼ
  - item: i5, łichííʼ, sphere dóó aooʼ
  - item: i0, łichííʼ, cone dóó aooʼ
  - item: i7, łichííʼ, sphere dóó no
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict aap if: theory1, qalipaat aamma tungujortoq
  - predict aap if: theory3, shape aamma sphere
  - predict aap if: theory2, shape aamma sphere
  - predict aap if: theory1, shape aamma cone
  - predict aap if: theory0, qalipaat aamma tungujortoq
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, qorsuk, cube aamma aap
  - item: i2, aappalajaarpoq, cube aamma no
  - item: i6, tungujortoq, cube aamma aap
  - item: i4, tungujortoq, sphere aamma no
  - item: i1, qorsuk, cone aamma aap
  - item: i5, aappalajaarpoq, sphere aamma aap
  - item: i0, aappalajaarpoq, cone aamma aap
  - item: i7, aappalajaarpoq, sphere aamma no
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict ya sapos: theory1, kala na blu
  - predict ya sapos: theory3, shape na sphere
  - predict ya sapos: theory2, shape na sphere
  - predict ya sapos: theory1, shape na cone
  - predict ya sapos: theory0, kala na blu
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, grinpela, cube na ya
  - item: i2, ret, cube na no
  - item: i6, blu, cube na ya
  - item: i4, blu, sphere na no
  - item: i1, grinpela, cone na ya
  - item: i5, ret, sphere na ya
  - item: i0, ret, cone na ya
  - item: i7, ret, sphere na no
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube yes
  - item: i2, màxke, cube no
  - item: i6, blue, cube yes
  - item: i4, blue, sphere no
  - item: i1, green, cone yes
  - item: i5, màxke, sphere yes
  - item: i0, màxke, cone yes
  - item: i7, màxke, sphere no
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, бос а blue
  - predict yes if: theory3, shape а sphere
  - predict yes if: theory2, shape а sphere
  - predict yes if: theory1, shape а cone
  - predict yes if: theory0, бос а blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, баьццара, cube а yes
  - item: i2, цӏе, cube а no
  - item: i6, blue, cube а yes
  - item: i4, blue, sphere а no
  - item: i1, баьццара, cone а yes
  - item: i5, цӏе, sphere а yes
  - item: i0, цӏе, cone а yes
  - item: i7, цӏе, sphere а no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - theory0 2 이론 크기
  - theory1 4 이론 크기
  - 예언하다 네 만약: theory1, 색깔 푸르다
  - 예언하다 네 만약: theory3, 모양 구체
  - 예언하다 네 만약: theory2, 모양 구체
  - 예언하다 네 만약: theory1, 모양 솔방울
  - 예언하다 네 만약: theory0, 색깔 푸르다
  - theory2 2 이론 크기
  - theory3 2 이론 크기
cost model: 예외 cost 3.
자료:
  - 아이템: i3, 녹색의, 정육면체 네
  - 아이템: i2, 빨갛다, 정육면체 아니요
  - 아이템: i6, 푸르다, 정육면체 네
  - 아이템: i4, 푸르다, 구체 아니요
  - 아이템: i1, 녹색의, 솔방울 네
  - 아이템: i5, 빨갛다, 구체 네
  - 아이템: i0, 빨갛다, 솔방울 네
  - 아이템: i7, 빨갛다, 구체 아니요
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - theory0 2 theory tamaina
  - theory1 4 theory tamaina
  - aurresan bai if: theory1, kolore eta urdin
  - aurresan bai if: theory3, shape eta esfera
  - aurresan bai if: theory2, shape eta esfera
  - aurresan bai if: theory1, shape eta pinaburu
  - aurresan bai if: theory0, kolore eta urdin
  - theory2 2 theory tamaina
  - theory3 2 theory tamaina
Cost model: exception kostatu 3.
Data:
  - item: i3, berde, kubo eta bai
  - item: i2, gorri, kubo eta no
  - item: i6, urdin, kubo eta bai
  - item: i4, urdin, esfera eta no
  - item: i1, berde, pinaburu eta bai
  - item: i5, gorri, esfera eta bai
  - item: i0, gorri, pinaburu eta bai
  - item: i7, gorri, esfera eta no
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - predict ᎥᎥ if: theory3, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory2, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i2, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
  - item: i6, ᏌᎪᏂᎨᎢ, cube ᎠᎴ ᎥᎥ
  - item: i4, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i1, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᎥᎥ
  - item: i5, ᎩᎦᎨ, sphere ᎠᎴ ᎥᎥ
  - item: i0, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
  - item: i7, ᎩᎦᎨ, sphere ᎠᎴ ᏝᏃ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory0 theory chhika 2
  - theory1 theory chhika 4
  - predict arí if: theory1, llimphi anqas
  - predict arí if: theory3, shape sphere
  - predict arí if: theory2, shape sphere
  - predict arí if: theory1, shape cone
  - predict arí if: theory0, llimphi anqas
  - theory2 theory chhika 2
  - theory3 theory chhika 2
Cost model: exception cost 3.
Data:
  - item: i3, waylla, cube arí
  - item: i2, puka, cube mánan
  - item: i6, anqas, cube arí
  - item: i4, anqas, sphere mánan
  - item: i1, waylla, cone arí
  - item: i5, puka, sphere arí
  - item: i0, puka, cone arí
  - item: i7, puka, sphere mánan
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 тиориэ size
  - theory1 4 тиориэ size
  - predict yes if: theory1, color ыкӏи blue
  - predict yes if: theory3, shape ыкӏи sphere
  - predict yes if: theory2, shape ыкӏи sphere
  - predict yes if: theory1, shape ыкӏи cone
  - predict yes if: theory0, color ыкӏи blue
  - theory2 2 тиориэ size
  - theory3 2 тиориэ size
Cost model: exception cost 3.
Data:
  - item: i3, уцышъо, cube ыкӏи yes
  - item: i2, плъыжьы, cube ыкӏи no
  - item: i6, blue, cube ыкӏи yes
  - item: i4, blue, sphere ыкӏи no
  - item: i1, уцышъо, cone ыкӏи yes
  - item: i5, плъыжьы, sphere ыкӏи yes
  - item: i0, плъыжьы, cone ыкӏи yes
  - item: i7, плъыжьы, sphere ыкӏи no
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, xim thiab xiav
  - predict yes if: theory3, shape thiab sphere
  - predict yes if: theory2, shape thiab sphere
  - predict yes if: theory1, shape thiab cone
  - predict yes if: theory0, xim thiab xiav
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception nqi 3.
Data:
  - item: i3, ntsuab, cube thiab yes
  - item: i2, liab, cube thiab no
  - item: i6, xiav, cube thiab yes
  - item: i4, xiav, sphere thiab no
  - item: i1, ntsuab, cone thiab yes
  - item: i5, liab, sphere thiab yes
  - item: i0, liab, cone thiab yes
  - item: i7, liab, sphere thiab no
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict ngai if: theory1, color blue
  - predict ngai if: theory3, shape sphere
  - predict ngai if: theory2, shape sphere
  - predict ngai if: theory1, shape cone
  - predict ngai if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube ngai
  - item: i2, red, cube woi
  - item: i6, blue, cube ngai
  - item: i4, blue, sphere woi
  - item: i1, green, cone ngai
  - item: i5, red, sphere ngai
  - item: i0, red, cone ngai
  - item: i7, red, sphere woi
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict i̠i if: theory1, color micha' blue
  - predict i̠i if: theory3, shape micha' sphere
  - predict i̠i if: theory2, shape micha' sphere
  - predict i̠i if: theory1, shape micha' cone
  - predict i̠i if: theory0, color micha' blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube micha' i̠i
  - item: i2, homa, cube micha' no
  - item: i6, blue, cube micha' i̠i
  - item: i4, blue, sphere micha' no
  - item: i1, green, cone micha' i̠i
  - item: i5, homa, sphere micha' i̠i
  - item: i0, homa, cone micha' i̠i
  - item: i7, homa, sphere micha' no
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict yes if: theory1, color カ blue
  - predict yes if: theory3, shape カ sphere
  - predict yes if: theory2, shape カ sphere
  - predict yes if: theory1, shape カ cone
  - predict yes if: theory0, color カ blue
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, green, cube カ yes
  - item: i2, フレ, cube カ ソモ
  - item: i6, blue, cube カ yes
  - item: i4, blue, sphere カ ソモ
  - item: i1, green, cone カ yes
  - item: i5, フレ, sphere カ yes
  - item: i0, フレ, cone カ yes
  - item: i7, フレ, sphere カ ソモ
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes эвыр: theory1, пэран ынкъам ныԓиԓиԓӄин
  - predict yes эвыр: theory3, shape ынкъам sphere
  - predict yes эвыр: theory2, shape ынкъам sphere
  - predict yes эвыр: theory1, shape ынкъам cone
  - predict yes эвыр: theory0, пэран ынкъам ныԓиԓиԓӄин
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube ынкъам yes
  - item: i2, чеԓгыԓьын, cube ынкъам no
  - item: i6, ныԓиԓиԓӄин, cube ынкъам yes
  - item: i4, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i1, green, cone ынкъам yes
  - item: i5, чеԓгыԓьын, sphere ынкъам yes
  - item: i0, чеԓгыԓьын, cone ынкъам yes
  - item: i7, чеԓгыԓьын, sphere ынкъам no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes wa: theory1, color yéetel blue
  - predict yes wa: theory3, shape yéetel sphere
  - predict yes wa: theory2, shape yéetel sphere
  - predict yes wa: theory1, shape yéetel cone
  - predict yes wa: theory0, color yéetel blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, yaʼax, cube yéetel yes
  - item: i2, chak, cube yéetel no
  - item: i6, blue, cube yéetel yes
  - item: i4, blue, sphere yéetel no
  - item: i1, yaʼax, cone yéetel yes
  - item: i5, chak, sphere yéetel yes
  - item: i0, chak, cone yéetel yes
  - item: i7, chak, sphere yéetel no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict hą if: theory1, color blue
  - predict hą if: theory3, shape sphere
  - predict hą if: theory2, shape sphere
  - predict hą if: theory1, shape cone
  - predict hą if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube hą
  - item: i2, red, cube no
  - item: i6, blue, cube hą
  - item: i4, blue, sphere no
  - item: i1, green, cone hą
  - item: i5, red, sphere hą
  - item: i0, red, cone hą
  - item: i7, red, sphere no
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict yes if: theory1, color na blue
  - predict yes if: theory3, shape na sphere
  - predict yes if: theory2, shape na sphere
  - predict yes if: theory1, shape na cone
  - predict yes if: theory0, color na blue
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, green, cube na yes
  - item: i2, red, cube na no
  - item: i6, blue, cube na yes
  - item: i4, blue, sphere na no
  - item: i1, green, cone na yes
  - item: i5, red, sphere na yes
  - item: i0, red, cone na yes
  - item: i7, red, sphere na no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict heẽ if: theory1, color blue
  - predict heẽ if: theory3, shape sphere
  - predict heẽ if: theory2, shape sphere
  - predict heẽ if: theory1, shape cone
  - predict heẽ if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube heẽ
  - item: i2, pytã, cube nahániri
  - item: i6, blue, cube heẽ
  - item: i4, blue, sphere nahániri
  - item: i1, green, cone heẽ
  - item: i5, pytã, sphere heẽ
  - item: i0, pytã, cone heẽ
  - item: i7, pytã, sphere nahániri
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict jisa if: theory1, sami larama
  - predict jisa if: theory3, shape sphere
  - predict jisa if: theory2, shape sphere
  - predict jisa if: theory1, shape cone
  - predict jisa if: theory0, sami larama
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, ch’uxña, cube jisa
  - item: i2, chupika, cube no
  - item: i6, larama, cube jisa
  - item: i4, larama, sphere no
  - item: i1, ch’uxña, cone jisa
  - item: i5, chupika, sphere jisa
  - item: i0, chupika, cone jisa
  - item: i7, chupika, sphere no
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory ʻǁnàhã 2
  - theory1 theory ʻǁnàhã 4
  - predict ʻée if: theory1, color ǂʻá blue
  - predict ʻée if: theory3, shape ǂʻá sphere
  - predict ʻée if: theory2, shape ǂʻá sphere
  - predict ʻée if: theory1, shape ǂʻá cone
  - predict ʻée if: theory0, color ǂʻá blue
  - theory2 theory ʻǁnàhã 2
  - theory3 theory ʻǁnàhã 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube ǂʻá ʻée
  - item: i2, red, cube ǂʻá ʻĩĩ
  - item: i6, blue, cube ǂʻá ʻée
  - item: i4, blue, sphere ǂʻá ʻĩĩ
  - item: i1, green, cone ǂʻá ʻée
  - item: i5, red, sphere ǂʻá ʻée
  - item: i0, red, cone ǂʻá ʻée
  - item: i7, red, sphere ǂʻá ʻĩĩ
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict yes if: theory1, вал blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, вал blue
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, green, cube yes
  - item: i2, red, cube no
  - item: i6, blue, cube yes
  - item: i4, blue, sphere no
  - item: i1, green, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube yes
  - item: i2, red, cube no
  - item: i6, blue, cube yes
  - item: i4, blue, sphere no
  - item: i1, green, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, color blue
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, green, cube yes
  - item: i2, red, cube no
  - item: i6, blue, cube yes
  - item: i4, blue, sphere no
  - item: i1, green, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict ee if: theory1, color blue
  - predict ee if: theory3, shape sphere
  - predict ee if: theory2, shape sphere
  - predict ee if: theory1, shape cone
  - predict ee if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube ee
  - item: i2, red, cube no
  - item: i6, blue, cube ee
  - item: i4, blue, sphere no
  - item: i1, green, cone ee
  - item: i5, red, sphere ee
  - item: i0, red, cone ee
  - item: i7, red, sphere no
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube yes
  - item: i2, red, cube inte
  - item: i6, blue, cube yes
  - item: i4, blue, sphere inte
  - item: i1, green, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere inte
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - theory0 théorie taille 2
  - theory1 théorie taille 4
  - prédire oui si: theory1, couleur et bleu
  - prédire oui si: theory3, forme et sphère
  - prédire oui si: theory2, forme et sphère
  - prédire oui si: theory1, forme et cône
  - prédire oui si: theory0, couleur et bleu
  - theory2 théorie taille 2
  - theory3 théorie taille 2
Coûts : exception coûter 3.
Les données sont:
  - article: i3, vert, cube et oui
  - article: i2, rouge, cube et no
  - article: i6, bleu, cube et oui
  - article: i4, bleu, sphère et no
  - article: i1, vert, cône et oui
  - article: i5, rouge, sphère et oui
  - article: i0, rouge, cône et oui
  - article: i7, rouge, sphère et no
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - theory0 teorio grandeco 2
  - theory1 teorio grandeco 4
  - predict jes se: theory1, koloro kaj blua
  - predict jes se: theory3, formi kaj sfero
  - predict jes se: theory2, formi kaj sfero
  - predict jes se: theory1, formi kaj strobilo
  - predict jes se: theory0, koloro kaj blua
  - theory2 teorio grandeco 2
  - theory3 teorio grandeco 2
Kosto pozistino: escepto kosti 3.
Datumo:
  - ero: i3, verda, kubo kaj jes
  - ero: i2, ruĝa, kubo kaj no
  - ero: i6, blua, kubo kaj jes
  - ero: i4, blua, sfero kaj no
  - ero: i1, verda, strobilo kaj jes
  - ero: i5, ruĝa, sfero kaj jes
  - ero: i0, ruĝa, strobilo kaj jes
  - ero: i7, ruĝa, sfero kaj no
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - theory0 elmélet méret 2
  - theory1 elmélet méret 4
  - megjósol igen ha: theory1, szín és kék
  - megjósol igen ha: theory3, alak és gömb
  - megjósol igen ha: theory2, alak és gömb
  - megjósol igen ha: theory1, alak és toboz
  - megjósol igen ha: theory0, szín és kék
  - theory2 elmélet méret 2
  - theory3 elmélet méret 2
Költség manöken: kivétel kerül 3.
Adat:
  - cikk: i3, zöld, kocka és igen
  - cikk: i2, piros, kocka és no
  - cikk: i6, kék, kocka és igen
  - cikk: i4, kék, gömb és no
  - cikk: i1, zöld, toboz és igen
  - cikk: i5, piros, gömb és igen
  - cikk: i0, piros, toboz és igen
  - cikk: i7, piros, gömb és no
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - theory0theory size2
  - theory1theory size4
  - predict係要是:theory1, 色及藍色嘅
  - predict係要是:theory3, 形狀及sphere
  - predict係要是:theory2, 形狀及sphere
  - predict係要是:theory1, 形狀及cone
  - predict係要是:theory0, 色及藍色嘅
  - theory2theory size2
  - theory3theory size2
cost model:exception cost:3.
數據:
  - item:i3, 綠色嘅, cube及係
  - item:i2, 紅, cube及no
  - item:i6, 藍色嘅, cube及係
  - item:i4, 藍色嘅, sphere及no
  - item:i1, 綠色嘅, cone及係
  - item:i5, 紅, sphere及係
  - item:i0, 紅, cone及係
  - item:i7, 紅, sphere及no
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, color とぅ blue
  - predict yes if: theory3, shape とぅ sphere
  - predict yes if: theory2, shape とぅ sphere
  - predict yes if: theory1, shape とぅ cone
  - predict yes if: theory0, color とぅ blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube とぅ yes
  - item: i2, red, cube とぅ no
  - item: i6, blue, cube とぅ yes
  - item: i4, blue, sphere とぅ no
  - item: i1, green, cone とぅ yes
  - item: i5, red, sphere とぅ yes
  - item: i0, red, cone とぅ yes
  - item: i7, red, sphere とぅ no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - theory0 теория мөлшер 2
  - theory1 теория мөлшер 4
  - predict иә егер: theory1, түс және көк
  - predict иә егер: theory3, нысан және сфера
  - predict иә егер: theory2, нысан және сфера
  - predict иә егер: theory1, нысан және бүршік
  - predict иә егер: theory0, түс және көк
  - theory2 теория мөлшер 2
  - theory3 теория мөлшер 2
Тұру үлгі: ерекшелік тұру 3.
Деректер:
  - зат: i3, жасыл, куб және иә
  - зат: i2, қызыл, куб және жоқ
  - зат: i6, көк, куб және иә
  - зат: i4, көк, сфера және жоқ
  - зат: i1, жасыл, бүршік және иә
  - зат: i5, қызыл, сфера және иә
  - зат: i0, қызыл, бүршік және иә
  - зат: i7, қызыл, сфера және жоқ
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - theory0 תּוֹרָה גֹּדֶל 2
  - theory1 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory1, צֶבַע וְ־ כָּחוֹל
  - predict כֵּן אִם: theory3, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory2, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory1, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory0, צֶבַע וְ־ כָּחוֹל
  - theory2 תּוֹרָה גֹּדֶל 2
  - theory3 תּוֹרָה גֹּדֶל 2
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 3.
נְתוּנִים:
  - אבזר: i3, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i2, אָדוֹם, קובייה וְ־ no
  - אבזר: i6, כָּחוֹל, קובייה וְ־ כֵּן
  - אבזר: i4, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i1, ירוק מקנאה, אצטרובל וְ־ כֵּן
  - אבזר: i5, אָדוֹם, כַּדּוּר וְ־ כֵּן
  - אבזר: i0, אָדוֹם, אצטרובל וְ־ כֵּן
  - אבזר: i7, אָדוֹם, כַּדּוּר וְ־ no
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict იახ if: theory1, color დო blue
  - predict იახ if: theory3, shape დო sphere
  - predict იახ if: theory2, shape დო sphere
  - predict იახ if: theory1, shape დო cone
  - predict იახ if: theory0, color დო blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube დო იახ
  - item: i2, red, cube დო no
  - item: i6, blue, cube დო იახ
  - item: i4, blue, sphere დო no
  - item: i1, green, cone დო იახ
  - item: i5, red, sphere დო იახ
  - item: i0, red, cone დო იახ
  - item: i7, red, sphere დო no
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - theory0 teori ukuran 2
  - theory1 teori ukuran 4
  - predict ya jika: theory1, warna dan biru
  - predict ya jika: theory3, bentuk dan bola
  - predict ya jika: theory2, bentuk dan bola
  - predict ya jika: theory1, bentuk dan kerucut
  - predict ya jika: theory0, warna dan biru
  - theory2 teori ukuran 2
  - theory3 teori ukuran 2
Cost model: pengecualian menghabiskan biaya 3.
Data:
  - benda: i3, hijau, kubus dan ya
  - benda: i2, merah, kubus dan no
  - benda: i6, biru, kubus dan ya
  - benda: i4, biru, bola dan no
  - benda: i1, hijau, kerucut dan ya
  - benda: i5, merah, bola dan ya
  - benda: i0, merah, kerucut dan ya
  - benda: i7, merah, bola dan no
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - theory0ទ្រឹស្ដីទំហំ2
  - theory1ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងខៀវ
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory2, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងកោណ
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងខៀវ
  - theory2ទ្រឹស្ដីទំហំ2
  - theory3ទ្រឹស្ដីទំហំ2
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:3.
ទិន្នន័យ:
  - item:i3, បៃតង, គូបនឹងច៎ា
  - item:i2, ក្រហម, គូបនឹងទេ
  - item:i6, ខៀវ, គូបនឹងច៎ា
  - item:i4, ខៀវ, គោលនឹងទេ
  - item:i1, បៃតង, កោណនឹងច៎ា
  - item:i5, ក្រហម, គោលនឹងច៎ា
  - item:i0, ក្រហម, កោណនឹងច៎ា
  - item:i7, ក្រហម, គោលនឹងទេ
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - theory0ທິດສະດີຂະຫນາດ2
  - theory1ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຟ້າ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະcone
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຟ້າ
  - theory2ທິດສະດີຂະຫນາດ2
  - theory3ທິດສະດີຂະຫນາດ2
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:3.
ຂໍ້ມູນ:
  - item:i3, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i2, ແດງ, ລູກບາດແລະno
  - item:i6, ຟ້າ, ລູກບາດແລະແມ່ນ
  - item:i4, ຟ້າ, ຄະດີແລະno
  - item:i1, ຂຽວ, coneແລະແມ່ນ
  - item:i5, ແດງ, ຄະດີແລະແມ່ນ
  - item:i0, ແດງ, coneແລະແມ່ນ
  - item:i7, ແດງ, ຄະດີແລະno
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict అవును అయితే: theory1, రంగు మరియు నీలము
  - predict అవును అయితే: theory3, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory2, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory1, ఆకారం మరియు cone
  - predict అవును అయితే: theory0, రంగు మరియు నీలము
  - theory2 2 theory size
  - theory3 2 theory size
cost model: exception cost 3.
data:
  - item: i3, పచ్చని, cube మరియు అవును
  - item: i2, ఎర్ర, cube మరియు no
  - item: i6, నీలము, cube మరియు అవును
  - item: i4, నీలము, గోళము మరియు no
  - item: i1, పచ్చని, cone మరియు అవును
  - item: i5, ఎర్ర, గోళము మరియు అవును
  - item: i0, ఎర్ర, cone మరియు అవును
  - item: i7, ఎర్ర, గోళము మరియు no
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict hẹn if: theory1, color dúdú
  - predict hẹn if: theory3, shape sphere
  - predict hẹn if: theory2, shape sphere
  - predict hẹn if: theory1, shape cone
  - predict hẹn if: theory0, color dúdú
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube bẹ́ẹ̀ ni
  - item: i2, pupa, cube rárá
  - item: i6, dúdú, cube bẹ́ẹ̀ ni
  - item: i4, dúdú, sphere rárá
  - item: i1, green, cone bẹ́ẹ̀ ni
  - item: i5, pupa, sphere bẹ́ẹ̀ ni
  - item: i0, pupa, cone bẹ́ẹ̀ ni
  - item: i7, pupa, sphere rárá
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict au if: theory1, color blue
  - predict au if: theory3, shape sphere
  - predict au if: theory2, shape sphere
  - predict au if: theory1, shape cone
  - predict au if: theory0, color blue
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, green, cube au
  - item: i2, łichíí, cube no
  - item: i6, blue, cube au
  - item: i4, blue, sphere no
  - item: i1, green, cone au
  - item: i5, łichíí, sphere au
  - item: i0, łichíí, cone au
  - item: i7, łichíí, sphere no
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
cost model: exception cost 3.
data:
  - item: i3, green, cube yes
  - item: i2, red, cube ᐋᒃᑲ
  - item: i6, blue, cube yes
  - item: i4, blue, sphere ᐋᒃᑲ
  - item: i1, green, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere ᐋᒃᑲ
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, color blaw
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, color blaw
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, grun, cube yes
  - item: i2, red, cube no
  - item: i6, blaw, cube yes
  - item: i4, blaw, sphere no
  - item: i1, grun, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - theory1 theory size 4
  - predict yes if: theory1, color blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, color blue
  - theory2 theory size 2
  - theory3 theory size 2
Cost model: exception cost 3.
Data:
  - item: i3, green, cube yes
  - item: i2, red, cube no
  - item: i6, blue, cube yes
  - item: i4, blue, sphere no
  - item: i1, green, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - theory1 4 theory size
  - predict yes if: theory1, ранг blue
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, shape sphere
  - predict yes if: theory1, shape cone
  - predict yes if: theory0, ранг blue
  - theory2 2 theory size
  - theory3 2 theory size
Cost model: exception cost 3.
Data:
  - item: i3, къацу, cube yes
  - item: i2, red, cube no
  - item: i6, blue, cube yes
  - item: i4, blue, sphere no
  - item: i1, къацу, cone yes
  - item: i5, red, sphere yes
  - item: i0, red, cone yes
  - item: i7, red, sphere no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - theory0 theory size 2
  - predicts yes if: theory0, shape and sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predicts yes if: theory1, colour and red
  - predicts yes if: theory1, shape and cone
  - theory1 theory size 4
  - predicts yes if: theory2, colour and red
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, colour and green
Costs: exception cost 3.
The data are:
  - item: i7, blue, sphere and no
  - item: i6, blue, cube and no
  - item: i4, green, sphere and no
  - item: i3, red, cone and no
  - item: i1, blue, sphere and no
  - item: i2, red, cube and yes
  - item: i5, green, sphere and yes
  - item: i0, green, cube and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - theory0 el tamaño de la teoría 2
  - predice sí si: theory0, forma y esfera
  - theory3 el tamaño de la teoría 2
  - theory2 el tamaño de la teoría 4
  - predice sí si: theory1, es de color y rojo
  - predice sí si: theory1, forma y cono
  - theory1 el tamaño de la teoría 4
  - predice sí si: theory2, es de color y rojo
  - predice sí si: theory3, forma y esfera
  - predice sí si: theory2, es de color y verde
Costes: exception el coste: 3.
Los datos son:
  - el elemento: i7, azul, esfera y no
  - el elemento: i6, azul, cubo y no
  - el elemento: i4, verde, esfera y no
  - el elemento: i3, rojo, cono y no
  - el elemento: i1, azul, esfera y no
  - el elemento: i2, rojo, cubo y sí
  - el elemento: i5, verde, esfera y sí
  - el elemento: i0, verde, cubo y no
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - theory0的理论规模是2
  - 预测是若：theory0、形状和球体
  - theory3的理论规模是2
  - theory2的理论规模是4
  - 预测是若：theory1、颜色和红色的
  - 预测是若：theory1、形状和圆锥
  - theory1的理论规模是4
  - 预测是若：theory2、颜色和红色的
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、颜色和绿色的
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i7、蓝色的、球体和否
  - 的物品是：i6、蓝色的、立方体和否
  - 的物品是：i4、绿色的、球体和否
  - 的物品是：i3、红色的、圆锥和否
  - 的物品是：i1、蓝色的、球体和否
  - 的物品是：i2、红色的、立方体和是
  - 的物品是：i5、绿色的、球体和是
  - 的物品是：i0、绿色的、立方体和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - theory0 2 kuram boyut
  - öngörür evet eğer: theory0, şekil ve küre
  - theory3 2 kuram boyut
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory1, renk ve kırmızı
  - öngörür evet eğer: theory1, şekil ve koni
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory2, renk ve kırmızı
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, renk ve yeşil
Cost model: exception maliyet: 3.
Veriler:
  - item: i7, mavi, küre ve hayır
  - item: i6, mavi, küp ve hayır
  - item: i4, yeşil, küre ve hayır
  - item: i3, kırmızı, koni ve hayır
  - item: i1, mavi, küre ve hayır
  - item: i2, kırmızı, küp ve evet
  - item: i5, yeşil, küre ve evet
  - item: i0, yeşil, küp ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - theory0 theory size 2
  - predict ndiyo kama: theory0, umbo na tufe
  - theory3 theory size 2
  - theory2 theory size 4
  - predict ndiyo kama: theory1, rangi na ekundu
  - predict ndiyo kama: theory1, umbo na koni
  - theory1 theory size 4
  - predict ndiyo kama: theory2, rangi na ekundu
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, rangi na kijani
Cost model: exception gharimu: 3.
Data:
  - item: i7, buluu, tufe na hapana
  - item: i6, buluu, mchemraba na hapana
  - item: i4, kijani, tufe na hapana
  - item: i3, ekundu, koni na hapana
  - item: i1, buluu, tufe na hapana
  - item: i2, ekundu, mchemraba na ndiyo
  - item: i5, kijani, tufe na ndiyo
  - item: i0, kijani, mchemraba na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - theory0 theory size 2
  - predicts yes if: theory0, shape and sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predicts yes if: theory1, colour and red
  - predicts yes if: theory1, shape and cone
  - theory1 theory size 4
  - predicts yes if: theory2, colour and red
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory2, colour and green
Costs: exception cost 3.
The data are:
  - item: i7, blue, sphere and no
  - item: i6, blue, cube and no
  - item: i4, green, sphere and no
  - item: i3, red, cone and no
  - item: i1, blue, sphere and no
  - item: i2, red, cube and yes
  - item: i5, green, sphere and yes
  - item: i0, green, cube and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(theory_size theory0 2) (predicts_yes_if theory0 shape sphere) (theory_size theory3 2) (theory_size theory2 4) (predicts_yes_if theory1 color red) (predicts_yes_if theory1 shape cone) (theory_size theory1 4) (predicts_yes_if theory2 color red) (predicts_yes_if theory3 shape sphere) (predicts_yes_if theory2 color green)], cost_model: [(exception_cost 3)], data: [(item i7 blue sphere no) (item i6 blue cube no) (item i4 green sphere no) (item i3 red cone no) (item i1 blue sphere no) (item i2 red cube yes) (item i5 green sphere yes) (item i0 green cube no)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - theory0 Theorie Größe 2
  - vorhersagen ja wenn: theory0, Form und Kugel
  - theory3 Theorie Größe 2
  - theory2 Theorie Größe 4
  - vorhersagen ja wenn: theory1, Farbe und rot
  - vorhersagen ja wenn: theory1, Form und Kegel
  - theory1 Theorie Größe 4
  - vorhersagen ja wenn: theory2, Farbe und rot
  - vorhersagen ja wenn: theory3, Form und Kugel
  - vorhersagen ja wenn: theory2, Farbe und grün
Kosten: Ausnahme kosten 3.
Die Daten sind:
  - Ding: i7, blau, Kugel und nein
  - Ding: i6, blau, Kubus und nein
  - Ding: i4, grün, Kugel und nein
  - Ding: i3, rot, Kegel und nein
  - Ding: i1, blau, Kugel und nein
  - Ding: i2, rot, Kubus und ja
  - Ding: i5, grün, Kugel und ja
  - Ding: i0, grün, Kubus und nein
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - theory0 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory0, о̏блӣк и сфе́ра
  - theory3 тѐо̄рија величѝна 2
  - theory2 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory1, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory1, о̏блӣк и stožac
  - theory1 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory2, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory3, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory2, бо̀ја и зѐлен
Trošak мо̀дел: изузе́так ко̏штати 3.
Податци:
  - item: i7, пла̑в, сфе́ра и no
  - item: i6, пла̑в, коцка и no
  - item: i4, зѐлен, сфе́ра и no
  - item: i3, цр̀вен, stožac и no
  - item: i1, пла̑в, сфе́ра и no
  - item: i2, цр̀вен, коцка и да
  - item: i5, зѐлен, сфе́ра и да
  - item: i0, зѐлен, коцка и no
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - theory0 teoria koko 2
  - ennakoida kyllä jos: theory0, muoto ja pallo
  - theory3 teoria koko 2
  - theory2 teoria koko 4
  - ennakoida kyllä jos: theory1, väri ja punainen
  - ennakoida kyllä jos: theory1, muoto ja kartio
  - theory1 teoria koko 4
  - ennakoida kyllä jos: theory2, väri ja punainen
  - ennakoida kyllä jos: theory3, muoto ja pallo
  - ennakoida kyllä jos: theory2, väri ja vihreä
Kustannus malli: poikkeus maksaa 3.
Tieto:
  - esine: i7, sininen, pallo ja ei
  - esine: i6, sininen, kuutio ja ei
  - esine: i4, vihreä, pallo ja ei
  - esine: i3, punainen, kartio ja ei
  - esine: i1, sininen, pallo ja ei
  - esine: i2, punainen, kuutio ja kyllä
  - esine: i5, vihreä, pallo ja kyllä
  - esine: i0, vihreä, kuutio ja ei
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - theory0的理论规模是2
  - 预测是若：theory0、形状和球体
  - theory3的理论规模是2
  - theory2的理论规模是4
  - 预测是若：theory1、颜色和红色的
  - 预测是若：theory1、形状和圆锥
  - theory1的理论规模是4
  - 预测是若：theory2、颜色和红色的
  - 预测是若：theory3、形状和球体
  - 预测是若：theory2、颜色和绿色的
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i7、蓝色的、球体和否
  - 的物品是：i6、蓝色的、立方体和否
  - 的物品是：i4、绿色的、球体和否
  - 的物品是：i3、红色的、圆锥和否
  - 的物品是：i1、蓝色的、球体和否
  - 的物品是：i2、红色的、立方体和是
  - 的物品是：i5、绿色的、球体和是
  - 的物品是：i0、绿色的、立方体和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - theory02理論大きさ
  - 予言するはいもし...:theory0, 形と球
  - theory32理論大きさ
  - theory24理論大きさ
  - 予言するはいもし...:theory1, 色と赤い
  - 予言するはいもし...:theory1, 形と松毬
  - theory14理論大きさ
  - 予言するはいもし...:theory2, 色と赤い
  - 予言するはいもし...:theory3, 形と球
  - 予言するはいもし...:theory2, 色と緑の
費用モデル:例外費やす:3.
資料:
  - 項目:i7, 青い, 球といいえ
  - 項目:i6, 青い, 立方体といいえ
  - 項目:i4, 緑の, 球といいえ
  - 項目:i3, 赤い, 松毬といいえ
  - 項目:i1, 青い, 球といいえ
  - 項目:i2, 赤い, 立方体とはい
  - 項目:i5, 緑の, 球とはい
  - 項目:i0, 緑の, 立方体といいえ
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - theory0 2 kuram boyut
  - öngörür evet eğer: theory0, şekil ve küre
  - theory3 2 kuram boyut
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory1, renk ve kırmızı
  - öngörür evet eğer: theory1, şekil ve koni
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory2, renk ve kırmızı
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory2, renk ve yeşil
Cost model: exception maliyet: 3.
Veriler:
  - item: i7, mavi, küre ve hayır
  - item: i6, mavi, küp ve hayır
  - item: i4, yeşil, küre ve hayır
  - item: i3, kırmızı, koni ve hayır
  - item: i1, mavi, küre ve hayır
  - item: i2, kırmızı, küp ve evet
  - item: i5, yeşil, küre ve evet
  - item: i0, yeşil, küp ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - نَظَرِيَّة مَقَاس theory0 2
  - تَنَبَّأَ نَعَم إِنْ: theory0، شَكْل وَـ كُرَة
  - نَظَرِيَّة مَقَاس theory3 2
  - نَظَرِيَّة مَقَاس theory2 4
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ مَخْرُوط
  - نَظَرِيَّة مَقَاس theory1 4
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَخْضَر
cost model: اِسْتِثْنَاء cost 3.
بَيَانَات:
  - بَنْد: i7، أَزْرَق، كُرَة وَـ no
  - بَنْد: i6، أَزْرَق، كَعْبَة وَـ no
  - بَنْد: i4، أَخْضَر، كُرَة وَـ no
  - بَنْد: i3، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i1، أَزْرَق، كُرَة وَـ no
  - بَنْد: i2، أَحْمَر، كَعْبَة وَـ نَعَم
  - بَنْد: i5، أَخْضَر، كُرَة وَـ نَعَم
  - بَنْد: i0، أَخْضَر، كَعْبَة وَـ no
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - theory0 2 თეორია ზომა
  - predict დიახ თუ: theory0, ფორმა და სფერო
  - theory3 2 თეორია ზომა
  - theory2 4 თეორია ზომა
  - predict დიახ თუ: theory1, ფერი და წითელი
  - predict დიახ თუ: theory1, ფორმა და გირჩა
  - theory1 4 თეორია ზომა
  - predict დიახ თუ: theory2, ფერი და წითელი
  - predict დიახ თუ: theory3, ფორმა და სფერო
  - predict დიახ თუ: theory2, ფერი და მწვანე
Ფასი მოდელი: გამონაკლისი ღირს 3.
Მონაცემები:
  - item: i7, ლურჯი, სფერო და no
  - item: i6, ლურჯი, კუბი და no
  - item: i4, მწვანე, სფერო და no
  - item: i3, წითელი, გირჩა და no
  - item: i1, ლურჯი, სფერო და no
  - item: i2, წითელი, კუბი და დიახ
  - item: i5, მწვანე, სფერო და დიახ
  - item: i0, მწვანე, კუბი და no
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - ariā size theory0 2
  - matakite āe mēnā: theory0, āhuahanga me poi
  - ariā size theory3 2
  - ariā size theory2 4
  - matakite āe mēnā: theory1, kano me red
  - matakite āe mēnā: theory1, āhuahanga me koeko
  - ariā size theory1 4
  - matakite āe mēnā: theory2, kano me red
  - matakite āe mēnā: theory3, āhuahanga me poi
  - matakite āe mēnā: theory2, kano me kākāriki
Cost model: hāungatanga cost 3.
Hōtuku:
  - tūemi: i7, kahurangi, poi me kāo
  - tūemi: i6, kahurangi, mataono me kāo
  - tūemi: i4, kākāriki, poi me kāo
  - tūemi: i3, red, koeko me kāo
  - tūemi: i1, kahurangi, poi me kāo
  - tūemi: i2, red, mataono me āe
  - tūemi: i5, kākāriki, poi me āe
  - tūemi: i0, kākāriki, mataono me kāo
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - theory0 lý thuyết kích thước 2
  - dự báo vâng nếu: theory0, hình dáng và mặt cầu
  - theory3 lý thuyết kích thước 2
  - theory2 lý thuyết kích thước 4
  - dự báo vâng nếu: theory1, màu và đỏ
  - dự báo vâng nếu: theory1, hình dáng và nón
  - theory1 lý thuyết kích thước 4
  - dự báo vâng nếu: theory2, màu và đỏ
  - dự báo vâng nếu: theory3, hình dáng và mặt cầu
  - dự báo vâng nếu: theory2, màu và green
Cost model: lệ ngoại cost 3.
Dữ liệu:
  - vật phẩm: i7, blue, mặt cầu và no
  - vật phẩm: i6, blue, lập phương và no
  - vật phẩm: i4, green, mặt cầu và no
  - vật phẩm: i3, đỏ, nón và no
  - vật phẩm: i1, blue, mặt cầu và no
  - vật phẩm: i2, đỏ, lập phương và vâng
  - vật phẩm: i5, green, mặt cầu và vâng
  - vật phẩm: i0, green, lập phương và no
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - theory0ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory0, รูปร่างและทรงกลม
  - theory3ทฤษฎีขนาด2
  - theory2ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory1, สีและแดง
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและกรวย
  - theory1ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory2, สีและแดง
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory2, สีและเขียว
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:3.
ข้อมูล:
  - item:i7, น้ำเงิน, ทรงกลมและไฮ้
  - item:i6, น้ำเงิน, ลูกบาศก์และไฮ้
  - item:i4, เขียว, ทรงกลมและไฮ้
  - item:i3, แดง, กรวยและไฮ้
  - item:i1, น้ำเงิน, ทรงกลมและไฮ้
  - item:i2, แดง, ลูกบาศก์และใช่
  - item:i5, เขียว, ทรงกลมและใช่
  - item:i0, เขียว, ลูกบาศก์และไฮ้
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - theory0 2 theory size
  - கணி ஆம் என்றால்: theory0, shape மற்றும் sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cone
  - theory1 4 theory size
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory3, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் பச்சை
cost model: exception செலவு 3.
தரவுகள்:
  - item: i7, நீலம், sphere மற்றும் no
  - item: i6, நீலம், cube மற்றும் no
  - item: i4, பச்சை, sphere மற்றும் no
  - item: i3, சிவப்பு, cone மற்றும் no
  - item: i1, நீலம், sphere மற்றும் no
  - item: i2, சிவப்பு, cube மற்றும் ஆம்
  - item: i5, பச்சை, sphere மற்றும் ஆம்
  - item: i0, பச்சை, cube மற்றும் no
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - theory0 theory size 2
  - predict ndiyo kama: theory0, umbo na tufe
  - theory3 theory size 2
  - theory2 theory size 4
  - predict ndiyo kama: theory1, rangi na ekundu
  - predict ndiyo kama: theory1, umbo na koni
  - theory1 theory size 4
  - predict ndiyo kama: theory2, rangi na ekundu
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory2, rangi na kijani
Cost model: exception gharimu: 3.
Data:
  - item: i7, buluu, tufe na hapana
  - item: i6, buluu, mchemraba na hapana
  - item: i4, kijani, tufe na hapana
  - item: i3, ekundu, koni na hapana
  - item: i1, buluu, tufe na hapana
  - item: i2, ekundu, mchemraba na ndiyo
  - item: i5, kijani, tufe na ndiyo
  - item: i0, kijani, mchemraba na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - theory0 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, shape dóó sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cone
  - theory1 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dinootłʼizh
Cost model: exception bą́ą́h ílį́ 3.
Data:
  - item: i7, dootłʼizh, sphere dóó no
  - item: i6, dootłʼizh, cube dóó no
  - item: i4, dinootłʼizh, sphere dóó no
  - item: i3, łichííʼ, cone dóó no
  - item: i1, dootłʼizh, sphere dóó no
  - item: i2, łichííʼ, cube dóó aooʼ
  - item: i5, dinootłʼizh, sphere dóó aooʼ
  - item: i0, dinootłʼizh, cube dóó no
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - theory0 2 theory size
  - predict aap if: theory0, shape aamma sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict aap if: theory1, qalipaat aamma aappalajaarpoq
  - predict aap if: theory1, shape aamma cone
  - theory1 4 theory size
  - predict aap if: theory2, qalipaat aamma aappalajaarpoq
  - predict aap if: theory3, shape aamma sphere
  - predict aap if: theory2, qalipaat aamma qorsuk
Cost model: exception cost 3.
Data:
  - item: i7, tungujortoq, sphere aamma no
  - item: i6, tungujortoq, cube aamma no
  - item: i4, qorsuk, sphere aamma no
  - item: i3, aappalajaarpoq, cone aamma no
  - item: i1, tungujortoq, sphere aamma no
  - item: i2, aappalajaarpoq, cube aamma aap
  - item: i5, qorsuk, sphere aamma aap
  - item: i0, qorsuk, cube aamma no
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict ya sapos: theory0, shape na sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict ya sapos: theory1, kala na ret
  - predict ya sapos: theory1, shape na cone
  - theory1 theory size 4
  - predict ya sapos: theory2, kala na ret
  - predict ya sapos: theory3, shape na sphere
  - predict ya sapos: theory2, kala na grinpela
Cost model: exception cost 3.
Data:
  - item: i7, blu, sphere na no
  - item: i6, blu, cube na no
  - item: i4, grinpela, sphere na no
  - item: i3, ret, cone na no
  - item: i1, blu, sphere na no
  - item: i2, ret, cube na ya
  - item: i5, grinpela, sphere na ya
  - item: i0, grinpela, cube na no
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, color màxke
  - predict yes if: theory1, shape cone
  - theory1 theory size 4
  - predict yes if: theory2, color màxke
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, màxke, cone no
  - item: i1, blue, sphere no
  - item: i2, màxke, cube yes
  - item: i5, green, sphere yes
  - item: i0, green, cube no
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape а sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, бос а цӏе
  - predict yes if: theory1, shape а cone
  - theory1 theory size 4
  - predict yes if: theory2, бос а цӏе
  - predict yes if: theory3, shape а sphere
  - predict yes if: theory2, бос а баьццара
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere а no
  - item: i6, blue, cube а no
  - item: i4, баьццара, sphere а no
  - item: i3, цӏе, cone а no
  - item: i1, blue, sphere а no
  - item: i2, цӏе, cube а yes
  - item: i5, баьццара, sphere а yes
  - item: i0, баьццара, cube а no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - theory0 2 이론 크기
  - 예언하다 네 만약: theory0, 모양 구체
  - theory3 2 이론 크기
  - theory2 4 이론 크기
  - 예언하다 네 만약: theory1, 색깔 빨갛다
  - 예언하다 네 만약: theory1, 모양 솔방울
  - theory1 4 이론 크기
  - 예언하다 네 만약: theory2, 색깔 빨갛다
  - 예언하다 네 만약: theory3, 모양 구체
  - 예언하다 네 만약: theory2, 색깔 녹색의
cost model: 예외 cost 3.
자료:
  - 아이템: i7, 푸르다, 구체 아니요
  - 아이템: i6, 푸르다, 정육면체 아니요
  - 아이템: i4, 녹색의, 구체 아니요
  - 아이템: i3, 빨갛다, 솔방울 아니요
  - 아이템: i1, 푸르다, 구체 아니요
  - 아이템: i2, 빨갛다, 정육면체 네
  - 아이템: i5, 녹색의, 구체 네
  - 아이템: i0, 녹색의, 정육면체 아니요
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - theory0 2 theory tamaina
  - aurresan bai if: theory0, shape eta esfera
  - theory3 2 theory tamaina
  - theory2 4 theory tamaina
  - aurresan bai if: theory1, kolore eta gorri
  - aurresan bai if: theory1, shape eta pinaburu
  - theory1 4 theory tamaina
  - aurresan bai if: theory2, kolore eta gorri
  - aurresan bai if: theory3, shape eta esfera
  - aurresan bai if: theory2, kolore eta berde
Cost model: exception kostatu 3.
Data:
  - item: i7, urdin, esfera eta no
  - item: i6, urdin, kubo eta no
  - item: i4, berde, esfera eta no
  - item: i3, gorri, pinaburu eta no
  - item: i1, urdin, esfera eta no
  - item: i2, gorri, kubo eta bai
  - item: i5, berde, esfera eta bai
  - item: i0, berde, kubo eta no
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - theory0 theory size 2
  - predict ᎥᎥ if: theory0, shape ᎠᎴ sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cone
  - theory1 theory size 4
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory3, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
Cost model: exception cost 3.
Data:
  - item: i7, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i6, ᏌᎪᏂᎨᎢ, cube ᎠᎴ ᏝᏃ
  - item: i4, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᏝᏃ
  - item: i3, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i1, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i2, ᎩᎦᎨ, cube ᎠᎴ ᎥᎥ
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᎥᎥ
  - item: i0, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory0 theory chhika 2
  - predict arí if: theory0, shape sphere
  - theory3 theory chhika 2
  - theory2 theory chhika 4
  - predict arí if: theory1, llimphi puka
  - predict arí if: theory1, shape cone
  - theory1 theory chhika 4
  - predict arí if: theory2, llimphi puka
  - predict arí if: theory3, shape sphere
  - predict arí if: theory2, llimphi waylla
Cost model: exception cost 3.
Data:
  - item: i7, anqas, sphere mánan
  - item: i6, anqas, cube mánan
  - item: i4, waylla, sphere mánan
  - item: i3, puka, cone mánan
  - item: i1, anqas, sphere mánan
  - item: i2, puka, cube arí
  - item: i5, waylla, sphere arí
  - item: i0, waylla, cube mánan
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 тиориэ size
  - predict yes if: theory0, shape ыкӏи sphere
  - theory3 2 тиориэ size
  - theory2 4 тиориэ size
  - predict yes if: theory1, color ыкӏи плъыжьы
  - predict yes if: theory1, shape ыкӏи cone
  - theory1 4 тиориэ size
  - predict yes if: theory2, color ыкӏи плъыжьы
  - predict yes if: theory3, shape ыкӏи sphere
  - predict yes if: theory2, color ыкӏи уцышъо
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere ыкӏи no
  - item: i6, blue, cube ыкӏи no
  - item: i4, уцышъо, sphere ыкӏи no
  - item: i3, плъыжьы, cone ыкӏи no
  - item: i1, blue, sphere ыкӏи no
  - item: i2, плъыжьы, cube ыкӏи yes
  - item: i5, уцышъо, sphere ыкӏи yes
  - item: i0, уцышъо, cube ыкӏи no
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape thiab sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, xim thiab liab
  - predict yes if: theory1, shape thiab cone
  - theory1 theory size 4
  - predict yes if: theory2, xim thiab liab
  - predict yes if: theory3, shape thiab sphere
  - predict yes if: theory2, xim thiab ntsuab
Cost model: exception nqi 3.
Data:
  - item: i7, xiav, sphere thiab no
  - item: i6, xiav, cube thiab no
  - item: i4, ntsuab, sphere thiab no
  - item: i3, liab, cone thiab no
  - item: i1, xiav, sphere thiab no
  - item: i2, liab, cube thiab yes
  - item: i5, ntsuab, sphere thiab yes
  - item: i0, ntsuab, cube thiab no
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict ngai if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict ngai if: theory1, color red
  - predict ngai if: theory1, shape cone
  - theory1 theory size 4
  - predict ngai if: theory2, color red
  - predict ngai if: theory3, shape sphere
  - predict ngai if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere woi
  - item: i6, blue, cube woi
  - item: i4, green, sphere woi
  - item: i3, red, cone woi
  - item: i1, blue, sphere woi
  - item: i2, red, cube ngai
  - item: i5, green, sphere ngai
  - item: i0, green, cube woi
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - theory0 theory size 2
  - predict i̠i if: theory0, shape micha' sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict i̠i if: theory1, color micha' homa
  - predict i̠i if: theory1, shape micha' cone
  - theory1 theory size 4
  - predict i̠i if: theory2, color micha' homa
  - predict i̠i if: theory3, shape micha' sphere
  - predict i̠i if: theory2, color micha' green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere micha' no
  - item: i6, blue, cube micha' no
  - item: i4, green, sphere micha' no
  - item: i3, homa, cone micha' no
  - item: i1, blue, sphere micha' no
  - item: i2, homa, cube micha' i̠i
  - item: i5, green, sphere micha' i̠i
  - item: i0, green, cube micha' no
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - theory0 2 theory size
  - predict yes if: theory0, shape カ sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict yes if: theory1, color カ フレ
  - predict yes if: theory1, shape カ cone
  - theory1 4 theory size
  - predict yes if: theory2, color カ フレ
  - predict yes if: theory3, shape カ sphere
  - predict yes if: theory2, color カ green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere カ ソモ
  - item: i6, blue, cube カ ソモ
  - item: i4, green, sphere カ ソモ
  - item: i3, フレ, cone カ ソモ
  - item: i1, blue, sphere カ ソモ
  - item: i2, フレ, cube カ yes
  - item: i5, green, sphere カ yes
  - item: i0, green, cube カ ソモ
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict yes эвыр: theory0, shape ынкъам sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes эвыр: theory1, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory1, shape ынкъам cone
  - theory1 theory size 4
  - predict yes эвыр: theory2, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory3, shape ынкъам sphere
  - predict yes эвыр: theory2, пэран ынкъам green
Cost model: exception cost 3.
Data:
  - item: i7, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i6, ныԓиԓиԓӄин, cube ынкъам no
  - item: i4, green, sphere ынкъам no
  - item: i3, чеԓгыԓьын, cone ынкъам no
  - item: i1, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i2, чеԓгыԓьын, cube ынкъам yes
  - item: i5, green, sphere ынкъам yes
  - item: i0, green, cube ынкъам no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict yes wa: theory0, shape yéetel sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes wa: theory1, color yéetel chak
  - predict yes wa: theory1, shape yéetel cone
  - theory1 theory size 4
  - predict yes wa: theory2, color yéetel chak
  - predict yes wa: theory3, shape yéetel sphere
  - predict yes wa: theory2, color yéetel yaʼax
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere yéetel no
  - item: i6, blue, cube yéetel no
  - item: i4, yaʼax, sphere yéetel no
  - item: i3, chak, cone yéetel no
  - item: i1, blue, sphere yéetel no
  - item: i2, chak, cube yéetel yes
  - item: i5, yaʼax, sphere yéetel yes
  - item: i0, yaʼax, cube yéetel no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict hą if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict hą if: theory1, color red
  - predict hą if: theory1, shape cone
  - theory1 theory size 4
  - predict hą if: theory2, color red
  - predict hą if: theory3, shape sphere
  - predict hą if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, red, cone no
  - item: i1, blue, sphere no
  - item: i2, red, cube hą
  - item: i5, green, sphere hą
  - item: i0, green, cube no
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - theory0 2 theory size
  - predict yes if: theory0, shape na sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict yes if: theory1, color na red
  - predict yes if: theory1, shape na cone
  - theory1 4 theory size
  - predict yes if: theory2, color na red
  - predict yes if: theory3, shape na sphere
  - predict yes if: theory2, color na green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere na no
  - item: i6, blue, cube na no
  - item: i4, green, sphere na no
  - item: i3, red, cone na no
  - item: i1, blue, sphere na no
  - item: i2, red, cube na yes
  - item: i5, green, sphere na yes
  - item: i0, green, cube na no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict heẽ if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict heẽ if: theory1, color pytã
  - predict heẽ if: theory1, shape cone
  - theory1 theory size 4
  - predict heẽ if: theory2, color pytã
  - predict heẽ if: theory3, shape sphere
  - predict heẽ if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere nahániri
  - item: i6, blue, cube nahániri
  - item: i4, green, sphere nahániri
  - item: i3, pytã, cone nahániri
  - item: i1, blue, sphere nahániri
  - item: i2, pytã, cube heẽ
  - item: i5, green, sphere heẽ
  - item: i0, green, cube nahániri
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - predict jisa if: theory0, shape sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict jisa if: theory1, sami chupika
  - predict jisa if: theory1, shape cone
  - theory1 4 theory size
  - predict jisa if: theory2, sami chupika
  - predict jisa if: theory3, shape sphere
  - predict jisa if: theory2, sami ch’uxña
Cost model: exception cost 3.
Data:
  - item: i7, larama, sphere no
  - item: i6, larama, cube no
  - item: i4, ch’uxña, sphere no
  - item: i3, chupika, cone no
  - item: i1, larama, sphere no
  - item: i2, chupika, cube jisa
  - item: i5, ch’uxña, sphere jisa
  - item: i0, ch’uxña, cube no
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory ʻǁnàhã 2
  - predict ʻée if: theory0, shape ǂʻá sphere
  - theory3 theory ʻǁnàhã 2
  - theory2 theory ʻǁnàhã 4
  - predict ʻée if: theory1, color ǂʻá red
  - predict ʻée if: theory1, shape ǂʻá cone
  - theory1 theory ʻǁnàhã 4
  - predict ʻée if: theory2, color ǂʻá red
  - predict ʻée if: theory3, shape ǂʻá sphere
  - predict ʻée if: theory2, color ǂʻá green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere ǂʻá ʻĩĩ
  - item: i6, blue, cube ǂʻá ʻĩĩ
  - item: i4, green, sphere ǂʻá ʻĩĩ
  - item: i3, red, cone ǂʻá ʻĩĩ
  - item: i1, blue, sphere ǂʻá ʻĩĩ
  - item: i2, red, cube ǂʻá ʻée
  - item: i5, green, sphere ǂʻá ʻée
  - item: i0, green, cube ǂʻá ʻĩĩ
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - predict yes if: theory0, shape sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict yes if: theory1, вал red
  - predict yes if: theory1, shape cone
  - theory1 4 theory size
  - predict yes if: theory2, вал red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, вал green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, red, cone no
  - item: i1, blue, sphere no
  - item: i2, red, cube yes
  - item: i5, green, sphere yes
  - item: i0, green, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, color red
  - predict yes if: theory1, shape cone
  - theory1 theory size 4
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, red, cone no
  - item: i1, blue, sphere no
  - item: i2, red, cube yes
  - item: i5, green, sphere yes
  - item: i0, green, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - predict yes if: theory0, shape sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict yes if: theory1, color red
  - predict yes if: theory1, shape cone
  - theory1 4 theory size
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, red, cone no
  - item: i1, blue, sphere no
  - item: i2, red, cube yes
  - item: i5, green, sphere yes
  - item: i0, green, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict ee if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict ee if: theory1, color red
  - predict ee if: theory1, shape cone
  - theory1 theory size 4
  - predict ee if: theory2, color red
  - predict ee if: theory3, shape sphere
  - predict ee if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, red, cone no
  - item: i1, blue, sphere no
  - item: i2, red, cube ee
  - item: i5, green, sphere ee
  - item: i0, green, cube no
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, color red
  - predict yes if: theory1, shape cone
  - theory1 theory size 4
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere inte
  - item: i6, blue, cube inte
  - item: i4, green, sphere inte
  - item: i3, red, cone inte
  - item: i1, blue, sphere inte
  - item: i2, red, cube yes
  - item: i5, green, sphere yes
  - item: i0, green, cube inte
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - theory0 théorie taille 2
  - prédire oui si: theory0, forme et sphère
  - theory3 théorie taille 2
  - theory2 théorie taille 4
  - prédire oui si: theory1, couleur et rouge
  - prédire oui si: theory1, forme et cône
  - theory1 théorie taille 4
  - prédire oui si: theory2, couleur et rouge
  - prédire oui si: theory3, forme et sphère
  - prédire oui si: theory2, couleur et vert
Coûts : exception coûter 3.
Les données sont:
  - article: i7, bleu, sphère et no
  - article: i6, bleu, cube et no
  - article: i4, vert, sphère et no
  - article: i3, rouge, cône et no
  - article: i1, bleu, sphère et no
  - article: i2, rouge, cube et oui
  - article: i5, vert, sphère et oui
  - article: i0, vert, cube et no
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - theory0 teorio grandeco 2
  - predict jes se: theory0, formi kaj sfero
  - theory3 teorio grandeco 2
  - theory2 teorio grandeco 4
  - predict jes se: theory1, koloro kaj ruĝa
  - predict jes se: theory1, formi kaj strobilo
  - theory1 teorio grandeco 4
  - predict jes se: theory2, koloro kaj ruĝa
  - predict jes se: theory3, formi kaj sfero
  - predict jes se: theory2, koloro kaj verda
Kosto pozistino: escepto kosti 3.
Datumo:
  - ero: i7, blua, sfero kaj no
  - ero: i6, blua, kubo kaj no
  - ero: i4, verda, sfero kaj no
  - ero: i3, ruĝa, strobilo kaj no
  - ero: i1, blua, sfero kaj no
  - ero: i2, ruĝa, kubo kaj jes
  - ero: i5, verda, sfero kaj jes
  - ero: i0, verda, kubo kaj no
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - theory0 elmélet méret 2
  - megjósol igen ha: theory0, alak és gömb
  - theory3 elmélet méret 2
  - theory2 elmélet méret 4
  - megjósol igen ha: theory1, szín és piros
  - megjósol igen ha: theory1, alak és toboz
  - theory1 elmélet méret 4
  - megjósol igen ha: theory2, szín és piros
  - megjósol igen ha: theory3, alak és gömb
  - megjósol igen ha: theory2, szín és zöld
Költség manöken: kivétel kerül 3.
Adat:
  - cikk: i7, kék, gömb és no
  - cikk: i6, kék, kocka és no
  - cikk: i4, zöld, gömb és no
  - cikk: i3, piros, toboz és no
  - cikk: i1, kék, gömb és no
  - cikk: i2, piros, kocka és igen
  - cikk: i5, zöld, gömb és igen
  - cikk: i0, zöld, kocka és no
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - theory0theory size2
  - predict係要是:theory0, 形狀及sphere
  - theory3theory size2
  - theory2theory size4
  - predict係要是:theory1, 色及紅
  - predict係要是:theory1, 形狀及cone
  - theory1theory size4
  - predict係要是:theory2, 色及紅
  - predict係要是:theory3, 形狀及sphere
  - predict係要是:theory2, 色及綠色嘅
cost model:exception cost:3.
數據:
  - item:i7, 藍色嘅, sphere及no
  - item:i6, 藍色嘅, cube及no
  - item:i4, 綠色嘅, sphere及no
  - item:i3, 紅, cone及no
  - item:i1, 藍色嘅, sphere及no
  - item:i2, 紅, cube及係
  - item:i5, 綠色嘅, sphere及係
  - item:i0, 綠色嘅, cube及no
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape とぅ sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, color とぅ red
  - predict yes if: theory1, shape とぅ cone
  - theory1 theory size 4
  - predict yes if: theory2, color とぅ red
  - predict yes if: theory3, shape とぅ sphere
  - predict yes if: theory2, color とぅ green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere とぅ no
  - item: i6, blue, cube とぅ no
  - item: i4, green, sphere とぅ no
  - item: i3, red, cone とぅ no
  - item: i1, blue, sphere とぅ no
  - item: i2, red, cube とぅ yes
  - item: i5, green, sphere とぅ yes
  - item: i0, green, cube とぅ no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - theory0 теория мөлшер 2
  - predict иә егер: theory0, нысан және сфера
  - theory3 теория мөлшер 2
  - theory2 теория мөлшер 4
  - predict иә егер: theory1, түс және қызыл
  - predict иә егер: theory1, нысан және бүршік
  - theory1 теория мөлшер 4
  - predict иә егер: theory2, түс және қызыл
  - predict иә егер: theory3, нысан және сфера
  - predict иә егер: theory2, түс және жасыл
Тұру үлгі: ерекшелік тұру 3.
Деректер:
  - зат: i7, көк, сфера және жоқ
  - зат: i6, көк, куб және жоқ
  - зат: i4, жасыл, сфера және жоқ
  - зат: i3, қызыл, бүршік және жоқ
  - зат: i1, көк, сфера және жоқ
  - зат: i2, қызыл, куб және иә
  - зат: i5, жасыл, сфера және иә
  - зат: i0, жасыл, куб және жоқ
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - theory0 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory0, צוּרָה וְ־ כַּדּוּר
  - theory3 תּוֹרָה גֹּדֶל 2
  - theory2 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory1, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory1, צוּרָה וְ־ אצטרובל
  - theory1 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory2, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory3, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory2, צֶבַע וְ־ ירוק מקנאה
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 3.
נְתוּנִים:
  - אבזר: i7, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i6, כָּחוֹל, קובייה וְ־ no
  - אבזר: i4, ירוק מקנאה, כַּדּוּר וְ־ no
  - אבזר: i3, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i1, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i2, אָדוֹם, קובייה וְ־ כֵּן
  - אבזר: i5, ירוק מקנאה, כַּדּוּר וְ־ כֵּן
  - אבזר: i0, ירוק מקנאה, קובייה וְ־ no
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - theory0 theory size 2
  - predict იახ if: theory0, shape დო sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict იახ if: theory1, color დო red
  - predict იახ if: theory1, shape დო cone
  - theory1 theory size 4
  - predict იახ if: theory2, color დო red
  - predict იახ if: theory3, shape დო sphere
  - predict იახ if: theory2, color დო green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere დო no
  - item: i6, blue, cube დო no
  - item: i4, green, sphere დო no
  - item: i3, red, cone დო no
  - item: i1, blue, sphere დო no
  - item: i2, red, cube დო იახ
  - item: i5, green, sphere დო იახ
  - item: i0, green, cube დო no
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - theory0 teori ukuran 2
  - predict ya jika: theory0, bentuk dan bola
  - theory3 teori ukuran 2
  - theory2 teori ukuran 4
  - predict ya jika: theory1, warna dan merah
  - predict ya jika: theory1, bentuk dan kerucut
  - theory1 teori ukuran 4
  - predict ya jika: theory2, warna dan merah
  - predict ya jika: theory3, bentuk dan bola
  - predict ya jika: theory2, warna dan hijau
Cost model: pengecualian menghabiskan biaya 3.
Data:
  - benda: i7, biru, bola dan no
  - benda: i6, biru, kubus dan no
  - benda: i4, hijau, bola dan no
  - benda: i3, merah, kerucut dan no
  - benda: i1, biru, bola dan no
  - benda: i2, merah, kubus dan ya
  - benda: i5, hijau, bola dan ya
  - benda: i0, hijau, kubus dan no
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - theory0ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory0, គ្រោងនឹងគោល
  - theory3ទ្រឹស្ដីទំហំ2
  - theory2ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងកោណ
  - theory1ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងបៃតង
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:3.
ទិន្នន័យ:
  - item:i7, ខៀវ, គោលនឹងទេ
  - item:i6, ខៀវ, គូបនឹងទេ
  - item:i4, បៃតង, គោលនឹងទេ
  - item:i3, ក្រហម, កោណនឹងទេ
  - item:i1, ខៀវ, គោលនឹងទេ
  - item:i2, ក្រហម, គូបនឹងច៎ា
  - item:i5, បៃតង, គោលនឹងច៎ា
  - item:i0, បៃតង, គូបនឹងទេ
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - theory0ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ຮູບຮ່າງແລະຄະດີ
  - theory3ທິດສະດີຂະຫນາດ2
  - theory2ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະcone
  - theory1ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຂຽວ
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:3.
ຂໍ້ມູນ:
  - item:i7, ຟ້າ, ຄະດີແລະno
  - item:i6, ຟ້າ, ລູກບາດແລະno
  - item:i4, ຂຽວ, ຄະດີແລະno
  - item:i3, ແດງ, coneແລະno
  - item:i1, ຟ້າ, ຄະດີແລະno
  - item:i2, ແດງ, ລູກບາດແລະແມ່ນ
  - item:i5, ຂຽວ, ຄະດີແລະແມ່ນ
  - item:i0, ຂຽວ, ລູກບາດແລະno
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - theory0 2 theory size
  - predict అవును అయితే: theory0, ఆకారం మరియు గోళము
  - theory3 2 theory size
  - theory2 4 theory size
  - predict అవును అయితే: theory1, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory1, ఆకారం మరియు cone
  - theory1 4 theory size
  - predict అవును అయితే: theory2, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory3, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory2, రంగు మరియు పచ్చని
cost model: exception cost 3.
data:
  - item: i7, నీలము, గోళము మరియు no
  - item: i6, నీలము, cube మరియు no
  - item: i4, పచ్చని, గోళము మరియు no
  - item: i3, ఎర్ర, cone మరియు no
  - item: i1, నీలము, గోళము మరియు no
  - item: i2, ఎర్ర, cube మరియు అవును
  - item: i5, పచ్చని, గోళము మరియు అవును
  - item: i0, పచ్చని, cube మరియు no
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - theory0 theory size 2
  - predict hẹn if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict hẹn if: theory1, color pupa
  - predict hẹn if: theory1, shape cone
  - theory1 theory size 4
  - predict hẹn if: theory2, color pupa
  - predict hẹn if: theory3, shape sphere
  - predict hẹn if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, dúdú, sphere rárá
  - item: i6, dúdú, cube rárá
  - item: i4, green, sphere rárá
  - item: i3, pupa, cone rárá
  - item: i1, dúdú, sphere rárá
  - item: i2, pupa, cube bẹ́ẹ̀ ni
  - item: i5, green, sphere bẹ́ẹ̀ ni
  - item: i0, green, cube rárá
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory0 2 theory size
  - predict au if: theory0, shape sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict au if: theory1, color łichíí
  - predict au if: theory1, shape cone
  - theory1 4 theory size
  - predict au if: theory2, color łichíí
  - predict au if: theory3, shape sphere
  - predict au if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, łichíí, cone no
  - item: i1, blue, sphere no
  - item: i2, łichíí, cube au
  - item: i5, green, sphere au
  - item: i0, green, cube no
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, color red
  - predict yes if: theory1, shape cone
  - theory1 theory size 4
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color green
cost model: exception cost 3.
data:
  - item: i7, blue, sphere ᐋᒃᑲ
  - item: i6, blue, cube ᐋᒃᑲ
  - item: i4, green, sphere ᐋᒃᑲ
  - item: i3, red, cone ᐋᒃᑲ
  - item: i1, blue, sphere ᐋᒃᑲ
  - item: i2, red, cube yes
  - item: i5, green, sphere yes
  - item: i0, green, cube ᐋᒃᑲ
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, color red
  - predict yes if: theory1, shape cone
  - theory1 theory size 4
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color grun
Cost model: exception cost 3.
Data:
  - item: i7, blaw, sphere no
  - item: i6, blaw, cube no
  - item: i4, grun, sphere no
  - item: i3, red, cone no
  - item: i1, blaw, sphere no
  - item: i2, red, cube yes
  - item: i5, grun, sphere yes
  - item: i0, grun, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 theory size 2
  - predict yes if: theory0, shape sphere
  - theory3 theory size 2
  - theory2 theory size 4
  - predict yes if: theory1, color red
  - predict yes if: theory1, shape cone
  - theory1 theory size 4
  - predict yes if: theory2, color red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, color green
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, green, sphere no
  - item: i3, red, cone no
  - item: i1, blue, sphere no
  - item: i2, red, cube yes
  - item: i5, green, sphere yes
  - item: i0, green, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory0 2 theory size
  - predict yes if: theory0, shape sphere
  - theory3 2 theory size
  - theory2 4 theory size
  - predict yes if: theory1, ранг red
  - predict yes if: theory1, shape cone
  - theory1 4 theory size
  - predict yes if: theory2, ранг red
  - predict yes if: theory3, shape sphere
  - predict yes if: theory2, ранг къацу
Cost model: exception cost 3.
Data:
  - item: i7, blue, sphere no
  - item: i6, blue, cube no
  - item: i4, къацу, sphere no
  - item: i3, red, cone no
  - item: i1, blue, sphere no
  - item: i2, red, cube yes
  - item: i5, къацу, sphere yes
  - item: i0, къацу, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - predicts yes if: theory2, colour and green
  - theory3 theory size 2
  - theory1 theory size 4
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory1, colour and red
  - predicts yes if: theory1, colour and green
  - theory2 theory size 2
  - theory0 theory size 4
  - predicts yes if: theory0, colour and blue
Costs: exception cost 2.
The data are:
  - item: i7, red, sphere and no
  - item: i2, red, cube and no
  - item: i5, red, cone and no
  - item: i6, red, cube and no
  - item: i3, green, cube and yes
  - item: i1, blue, cone and yes
  - item: i4, blue, cone and no
  - item: i0, blue, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - predice sí si: theory2, es de color y verde
  - theory3 el tamaño de la teoría 2
  - theory1 el tamaño de la teoría 4
  - predice sí si: theory3, forma y esfera
  - predice sí si: theory0, es de color y rojo
  - predice sí si: theory1, es de color y rojo
  - predice sí si: theory1, es de color y verde
  - theory2 el tamaño de la teoría 2
  - theory0 el tamaño de la teoría 4
  - predice sí si: theory0, es de color y azul
Costes: exception el coste: 2.
Los datos son:
  - el elemento: i7, rojo, esfera y no
  - el elemento: i2, rojo, cubo y no
  - el elemento: i5, rojo, cono y no
  - el elemento: i6, rojo, cubo y no
  - el elemento: i3, verde, cubo y sí
  - el elemento: i1, azul, cono y sí
  - el elemento: i4, azul, cono y no
  - el elemento: i0, azul, cubo y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - 预测是若：theory2、颜色和绿色的
  - theory3的理论规模是2
  - theory1的理论规模是4
  - 预测是若：theory3、形状和球体
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory1、颜色和红色的
  - 预测是若：theory1、颜色和绿色的
  - theory2的理论规模是2
  - theory0的理论规模是4
  - 预测是若：theory0、颜色和蓝色的
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i7、红色的、球体和否
  - 的物品是：i2、红色的、立方体和否
  - 的物品是：i5、红色的、圆锥和否
  - 的物品是：i6、红色的、立方体和否
  - 的物品是：i3、绿色的、立方体和是
  - 的物品是：i1、蓝色的、圆锥和是
  - 的物品是：i4、蓝色的、圆锥和否
  - 的物品是：i0、蓝色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - öngörür evet eğer: theory2, renk ve yeşil
  - theory3 2 kuram boyut
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory1, renk ve kırmızı
  - öngörür evet eğer: theory1, renk ve yeşil
  - theory2 2 kuram boyut
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, renk ve mavi
Cost model: exception maliyet: 2.
Veriler:
  - item: i7, kırmızı, küre ve hayır
  - item: i2, kırmızı, küp ve hayır
  - item: i5, kırmızı, koni ve hayır
  - item: i6, kırmızı, küp ve hayır
  - item: i3, yeşil, küp ve evet
  - item: i1, mavi, koni ve evet
  - item: i4, mavi, koni ve hayır
  - item: i0, mavi, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na kijani
  - theory3 theory size 2
  - theory1 theory size 4
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory1, rangi na ekundu
  - predict ndiyo kama: theory1, rangi na kijani
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ndiyo kama: theory0, rangi na buluu
Cost model: exception gharimu: 2.
Data:
  - item: i7, ekundu, tufe na hapana
  - item: i2, ekundu, mchemraba na hapana
  - item: i5, ekundu, koni na hapana
  - item: i6, ekundu, mchemraba na hapana
  - item: i3, kijani, mchemraba na ndiyo
  - item: i1, buluu, koni na ndiyo
  - item: i4, buluu, koni na hapana
  - item: i0, buluu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - predicts yes if: theory2, colour and green
  - theory3 theory size 2
  - theory1 theory size 4
  - predicts yes if: theory3, shape and sphere
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory1, colour and red
  - predicts yes if: theory1, colour and green
  - theory2 theory size 2
  - theory0 theory size 4
  - predicts yes if: theory0, colour and blue
Costs: exception cost 2.
The data are:
  - item: i7, red, sphere and no
  - item: i2, red, cube and no
  - item: i5, red, cone and no
  - item: i6, red, cube and no
  - item: i3, green, cube and yes
  - item: i1, blue, cone and yes
  - item: i4, blue, cone and no
  - item: i0, blue, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(predicts_yes_if theory2 color green) (theory_size theory3 2) (theory_size theory1 4) (predicts_yes_if theory3 shape sphere) (predicts_yes_if theory0 color red) (predicts_yes_if theory1 color red) (predicts_yes_if theory1 color green) (theory_size theory2 2) (theory_size theory0 4) (predicts_yes_if theory0 color blue)], cost_model: [(exception_cost 2)], data: [(item i7 red sphere no) (item i2 red cube no) (item i5 red cone no) (item i6 red cube no) (item i3 green cube yes) (item i1 blue cone yes) (item i4 blue cone no) (item i0 blue cube yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - vorhersagen ja wenn: theory2, Farbe und grün
  - theory3 Theorie Größe 2
  - theory1 Theorie Größe 4
  - vorhersagen ja wenn: theory3, Form und Kugel
  - vorhersagen ja wenn: theory0, Farbe und rot
  - vorhersagen ja wenn: theory1, Farbe und rot
  - vorhersagen ja wenn: theory1, Farbe und grün
  - theory2 Theorie Größe 2
  - theory0 Theorie Größe 4
  - vorhersagen ja wenn: theory0, Farbe und blau
Kosten: Ausnahme kosten 2.
Die Daten sind:
  - Ding: i7, rot, Kugel und nein
  - Ding: i2, rot, Kubus und nein
  - Ding: i5, rot, Kegel und nein
  - Ding: i6, rot, Kubus und nein
  - Ding: i3, grün, Kubus und ja
  - Ding: i1, blau, Kegel und ja
  - Ding: i4, blau, Kegel und nein
  - Ding: i0, blau, Kubus und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - predvidjeti да ȁko: theory2, бо̀ја и зѐлен
  - theory3 тѐо̄рија величѝна 2
  - theory1 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory3, о̏блӣк и сфе́ра
  - predvidjeti да ȁko: theory0, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory1, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory1, бо̀ја и зѐлен
  - theory2 тѐо̄рија величѝна 2
  - theory0 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory0, бо̀ја и пла̑в
Trošak мо̀дел: изузе́так ко̏штати 2.
Податци:
  - item: i7, цр̀вен, сфе́ра и no
  - item: i2, цр̀вен, коцка и no
  - item: i5, цр̀вен, stožac и no
  - item: i6, цр̀вен, коцка и no
  - item: i3, зѐлен, коцка и да
  - item: i1, пла̑в, stožac и да
  - item: i4, пла̑в, stožac и no
  - item: i0, пла̑в, коцка и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - ennakoida kyllä jos: theory2, väri ja vihreä
  - theory3 teoria koko 2
  - theory1 teoria koko 4
  - ennakoida kyllä jos: theory3, muoto ja pallo
  - ennakoida kyllä jos: theory0, väri ja punainen
  - ennakoida kyllä jos: theory1, väri ja punainen
  - ennakoida kyllä jos: theory1, väri ja vihreä
  - theory2 teoria koko 2
  - theory0 teoria koko 4
  - ennakoida kyllä jos: theory0, väri ja sininen
Kustannus malli: poikkeus maksaa 2.
Tieto:
  - esine: i7, punainen, pallo ja ei
  - esine: i2, punainen, kuutio ja ei
  - esine: i5, punainen, kartio ja ei
  - esine: i6, punainen, kuutio ja ei
  - esine: i3, vihreä, kuutio ja kyllä
  - esine: i1, sininen, kartio ja kyllä
  - esine: i4, sininen, kartio ja ei
  - esine: i0, sininen, kuutio ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - 预测是若：theory2、颜色和绿色的
  - theory3的理论规模是2
  - theory1的理论规模是4
  - 预测是若：theory3、形状和球体
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory1、颜色和红色的
  - 预测是若：theory1、颜色和绿色的
  - theory2的理论规模是2
  - theory0的理论规模是4
  - 预测是若：theory0、颜色和蓝色的
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i7、红色的、球体和否
  - 的物品是：i2、红色的、立方体和否
  - 的物品是：i5、红色的、圆锥和否
  - 的物品是：i6、红色的、立方体和否
  - 的物品是：i3、绿色的、立方体和是
  - 的物品是：i1、蓝色的、圆锥和是
  - 的物品是：i4、蓝色的、圆锥和否
  - 的物品是：i0、蓝色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - 予言するはいもし...:theory2, 色と緑の
  - theory32理論大きさ
  - theory14理論大きさ
  - 予言するはいもし...:theory3, 形と球
  - 予言するはいもし...:theory0, 色と赤い
  - 予言するはいもし...:theory1, 色と赤い
  - 予言するはいもし...:theory1, 色と緑の
  - theory22理論大きさ
  - theory04理論大きさ
  - 予言するはいもし...:theory0, 色と青い
費用モデル:例外費やす:2.
資料:
  - 項目:i7, 赤い, 球といいえ
  - 項目:i2, 赤い, 立方体といいえ
  - 項目:i5, 赤い, 松毬といいえ
  - 項目:i6, 赤い, 立方体といいえ
  - 項目:i3, 緑の, 立方体とはい
  - 項目:i1, 青い, 松毬とはい
  - 項目:i4, 青い, 松毬といいえ
  - 項目:i0, 青い, 立方体とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - öngörür evet eğer: theory2, renk ve yeşil
  - theory3 2 kuram boyut
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory3, şekil ve küre
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory1, renk ve kırmızı
  - öngörür evet eğer: theory1, renk ve yeşil
  - theory2 2 kuram boyut
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, renk ve mavi
Cost model: exception maliyet: 2.
Veriler:
  - item: i7, kırmızı, küre ve hayır
  - item: i2, kırmızı, küp ve hayır
  - item: i5, kırmızı, koni ve hayır
  - item: i6, kırmızı, küp ve hayır
  - item: i3, yeşil, küp ve evet
  - item: i1, mavi, koni ve evet
  - item: i4, mavi, koni ve hayır
  - item: i0, mavi, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory3 2
  - نَظَرِيَّة مَقَاس theory1 4
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كُرَة
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory2 2
  - نَظَرِيَّة مَقَاس theory0 4
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَزْرَق
cost model: اِسْتِثْنَاء cost 2.
بَيَانَات:
  - بَنْد: i7، أَحْمَر، كُرَة وَـ no
  - بَنْد: i2، أَحْمَر، كَعْبَة وَـ no
  - بَنْد: i5، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i6، أَحْمَر، كَعْبَة وَـ no
  - بَنْد: i3، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i1، أَزْرَق، مَخْرُوط وَـ نَعَم
  - بَنْد: i4، أَزْرَق، مَخْرُوط وَـ no
  - بَنْد: i0، أَزْرَق، كَعْبَة وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - predict დიახ თუ: theory2, ფერი და მწვანე
  - theory3 2 თეორია ზომა
  - theory1 4 თეორია ზომა
  - predict დიახ თუ: theory3, ფორმა და სფერო
  - predict დიახ თუ: theory0, ფერი და წითელი
  - predict დიახ თუ: theory1, ფერი და წითელი
  - predict დიახ თუ: theory1, ფერი და მწვანე
  - theory2 2 თეორია ზომა
  - theory0 4 თეორია ზომა
  - predict დიახ თუ: theory0, ფერი და ლურჯი
Ფასი მოდელი: გამონაკლისი ღირს 2.
Მონაცემები:
  - item: i7, წითელი, სფერო და no
  - item: i2, წითელი, კუბი და no
  - item: i5, წითელი, გირჩა და no
  - item: i6, წითელი, კუბი და no
  - item: i3, მწვანე, კუბი და დიახ
  - item: i1, ლურჯი, გირჩა და დიახ
  - item: i4, ლურჯი, გირჩა და no
  - item: i0, ლურჯი, კუბი და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - matakite āe mēnā: theory2, kano me kākāriki
  - ariā size theory3 2
  - ariā size theory1 4
  - matakite āe mēnā: theory3, āhuahanga me poi
  - matakite āe mēnā: theory0, kano me red
  - matakite āe mēnā: theory1, kano me red
  - matakite āe mēnā: theory1, kano me kākāriki
  - ariā size theory2 2
  - ariā size theory0 4
  - matakite āe mēnā: theory0, kano me kahurangi
Cost model: hāungatanga cost 2.
Hōtuku:
  - tūemi: i7, red, poi me kāo
  - tūemi: i2, red, mataono me kāo
  - tūemi: i5, red, koeko me kāo
  - tūemi: i6, red, mataono me kāo
  - tūemi: i3, kākāriki, mataono me āe
  - tūemi: i1, kahurangi, koeko me āe
  - tūemi: i4, kahurangi, koeko me kāo
  - tūemi: i0, kahurangi, mataono me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - dự báo vâng nếu: theory2, màu và green
  - theory3 lý thuyết kích thước 2
  - theory1 lý thuyết kích thước 4
  - dự báo vâng nếu: theory3, hình dáng và mặt cầu
  - dự báo vâng nếu: theory0, màu và đỏ
  - dự báo vâng nếu: theory1, màu và đỏ
  - dự báo vâng nếu: theory1, màu và green
  - theory2 lý thuyết kích thước 2
  - theory0 lý thuyết kích thước 4
  - dự báo vâng nếu: theory0, màu và blue
Cost model: lệ ngoại cost 2.
Dữ liệu:
  - vật phẩm: i7, đỏ, mặt cầu và no
  - vật phẩm: i2, đỏ, lập phương và no
  - vật phẩm: i5, đỏ, nón và no
  - vật phẩm: i6, đỏ, lập phương và no
  - vật phẩm: i3, green, lập phương và vâng
  - vật phẩm: i1, blue, nón và vâng
  - vật phẩm: i4, blue, nón và no
  - vật phẩm: i0, blue, lập phương và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - ทำนายใช่ถ้า หาก:theory2, สีและเขียว
  - theory3ทฤษฎีขนาด2
  - theory1ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและทรงกลม
  - ทำนายใช่ถ้า หาก:theory0, สีและแดง
  - ทำนายใช่ถ้า หาก:theory1, สีและแดง
  - ทำนายใช่ถ้า หาก:theory1, สีและเขียว
  - theory2ทฤษฎีขนาด2
  - theory0ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory0, สีและน้ำเงิน
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:2.
ข้อมูล:
  - item:i7, แดง, ทรงกลมและไฮ้
  - item:i2, แดง, ลูกบาศก์และไฮ้
  - item:i5, แดง, กรวยและไฮ้
  - item:i6, แดง, ลูกบาศก์และไฮ้
  - item:i3, เขียว, ลูกบาศก์และใช่
  - item:i1, น้ำเงิน, กรวยและใช่
  - item:i4, น้ำเงิน, กรวยและไฮ้
  - item:i0, น้ำเงิน, ลูกบาศก์และใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் பச்சை
  - theory3 2 theory size
  - theory1 4 theory size
  - கணி ஆம் என்றால்: theory3, shape மற்றும் sphere
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் பச்சை
  - theory2 2 theory size
  - theory0 4 theory size
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் நீலம்
cost model: exception செலவு 2.
தரவுகள்:
  - item: i7, சிவப்பு, sphere மற்றும் no
  - item: i2, சிவப்பு, cube மற்றும் no
  - item: i5, சிவப்பு, cone மற்றும் no
  - item: i6, சிவப்பு, cube மற்றும் no
  - item: i3, பச்சை, cube மற்றும் ஆம்
  - item: i1, நீலம், cone மற்றும் ஆம்
  - item: i4, நீலம், cone மற்றும் no
  - item: i0, நீலம், cube மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na kijani
  - theory3 theory size 2
  - theory1 theory size 4
  - predict ndiyo kama: theory3, umbo na tufe
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory1, rangi na ekundu
  - predict ndiyo kama: theory1, rangi na kijani
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ndiyo kama: theory0, rangi na buluu
Cost model: exception gharimu: 2.
Data:
  - item: i7, ekundu, tufe na hapana
  - item: i2, ekundu, mchemraba na hapana
  - item: i5, ekundu, koni na hapana
  - item: i6, ekundu, mchemraba na hapana
  - item: i3, kijani, mchemraba na ndiyo
  - item: i1, buluu, koni na ndiyo
  - item: i4, buluu, koni na hapana
  - item: i0, buluu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory3 2 theory size
  - theory1 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó sphere
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory2 2 theory size
  - theory0 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dootłʼizh
Cost model: exception bą́ą́h ílį́ 2.
Data:
  - item: i7, łichííʼ, sphere dóó no
  - item: i2, łichííʼ, cube dóó no
  - item: i5, łichííʼ, cone dóó no
  - item: i6, łichííʼ, cube dóó no
  - item: i3, dinootłʼizh, cube dóó aooʼ
  - item: i1, dootłʼizh, cone dóó aooʼ
  - item: i4, dootłʼizh, cone dóó no
  - item: i0, dootłʼizh, cube dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - predict aap if: theory2, qalipaat aamma qorsuk
  - theory3 2 theory size
  - theory1 4 theory size
  - predict aap if: theory3, shape aamma sphere
  - predict aap if: theory0, qalipaat aamma aappalajaarpoq
  - predict aap if: theory1, qalipaat aamma aappalajaarpoq
  - predict aap if: theory1, qalipaat aamma qorsuk
  - theory2 2 theory size
  - theory0 4 theory size
  - predict aap if: theory0, qalipaat aamma tungujortoq
Cost model: exception cost 2.
Data:
  - item: i7, aappalajaarpoq, sphere aamma no
  - item: i2, aappalajaarpoq, cube aamma no
  - item: i5, aappalajaarpoq, cone aamma no
  - item: i6, aappalajaarpoq, cube aamma no
  - item: i3, qorsuk, cube aamma aap
  - item: i1, tungujortoq, cone aamma aap
  - item: i4, tungujortoq, cone aamma no
  - item: i0, tungujortoq, cube aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ya sapos: theory2, kala na grinpela
  - theory3 theory size 2
  - theory1 theory size 4
  - predict ya sapos: theory3, shape na sphere
  - predict ya sapos: theory0, kala na ret
  - predict ya sapos: theory1, kala na ret
  - predict ya sapos: theory1, kala na grinpela
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ya sapos: theory0, kala na blu
Cost model: exception cost 2.
Data:
  - item: i7, ret, sphere na no
  - item: i2, ret, cube na no
  - item: i5, ret, cone na no
  - item: i6, ret, cube na no
  - item: i3, grinpela, cube na ya
  - item: i1, blu, cone na ya
  - item: i4, blu, cone na no
  - item: i0, blu, cube na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, color màxke
  - predict yes if: theory1, color màxke
  - predict yes if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, màxke, sphere no
  - item: i2, màxke, cube no
  - item: i5, màxke, cone no
  - item: i6, màxke, cube no
  - item: i3, green, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone no
  - item: i0, blue, cube yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - predict yes if: theory2, бос а баьццара
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape а sphere
  - predict yes if: theory0, бос а цӏе
  - predict yes if: theory1, бос а цӏе
  - predict yes if: theory1, бос а баьццара
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, бос а blue
Cost model: exception cost 2.
Data:
  - item: i7, цӏе, sphere а no
  - item: i2, цӏе, cube а no
  - item: i5, цӏе, cone а no
  - item: i6, цӏе, cube а no
  - item: i3, баьццара, cube а yes
  - item: i1, blue, cone а yes
  - item: i4, blue, cone а no
  - item: i0, blue, cube а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - 예언하다 네 만약: theory2, 색깔 녹색의
  - theory3 2 이론 크기
  - theory1 4 이론 크기
  - 예언하다 네 만약: theory3, 모양 구체
  - 예언하다 네 만약: theory0, 색깔 빨갛다
  - 예언하다 네 만약: theory1, 색깔 빨갛다
  - 예언하다 네 만약: theory1, 색깔 녹색의
  - theory2 2 이론 크기
  - theory0 4 이론 크기
  - 예언하다 네 만약: theory0, 색깔 푸르다
cost model: 예외 cost 2.
자료:
  - 아이템: i7, 빨갛다, 구체 아니요
  - 아이템: i2, 빨갛다, 정육면체 아니요
  - 아이템: i5, 빨갛다, 솔방울 아니요
  - 아이템: i6, 빨갛다, 정육면체 아니요
  - 아이템: i3, 녹색의, 정육면체 네
  - 아이템: i1, 푸르다, 솔방울 네
  - 아이템: i4, 푸르다, 솔방울 아니요
  - 아이템: i0, 푸르다, 정육면체 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - aurresan bai if: theory2, kolore eta berde
  - theory3 2 theory tamaina
  - theory1 4 theory tamaina
  - aurresan bai if: theory3, shape eta esfera
  - aurresan bai if: theory0, kolore eta gorri
  - aurresan bai if: theory1, kolore eta gorri
  - aurresan bai if: theory1, kolore eta berde
  - theory2 2 theory tamaina
  - theory0 4 theory tamaina
  - aurresan bai if: theory0, kolore eta urdin
Cost model: exception kostatu 2.
Data:
  - item: i7, gorri, esfera eta no
  - item: i2, gorri, kubo eta no
  - item: i5, gorri, pinaburu eta no
  - item: i6, gorri, kubo eta no
  - item: i3, berde, kubo eta bai
  - item: i1, urdin, pinaburu eta bai
  - item: i4, urdin, pinaburu eta no
  - item: i0, urdin, kubo eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory3 theory size 2
  - theory1 theory size 4
  - predict ᎥᎥ if: theory3, shape ᎠᎴ sphere
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
Cost model: exception cost 2.
Data:
  - item: i7, ᎩᎦᎨ, sphere ᎠᎴ ᏝᏃ
  - item: i2, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
  - item: i5, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i6, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
  - item: i3, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i1, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᎥᎥ
  - item: i4, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᏝᏃ
  - item: i0, ᏌᎪᏂᎨᎢ, cube ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict arí if: theory2, llimphi waylla
  - theory3 theory chhika 2
  - theory1 theory chhika 4
  - predict arí if: theory3, shape sphere
  - predict arí if: theory0, llimphi puka
  - predict arí if: theory1, llimphi puka
  - predict arí if: theory1, llimphi waylla
  - theory2 theory chhika 2
  - theory0 theory chhika 4
  - predict arí if: theory0, llimphi anqas
Cost model: exception cost 2.
Data:
  - item: i7, puka, sphere mánan
  - item: i2, puka, cube mánan
  - item: i5, puka, cone mánan
  - item: i6, puka, cube mánan
  - item: i3, waylla, cube arí
  - item: i1, anqas, cone arí
  - item: i4, anqas, cone mánan
  - item: i0, anqas, cube arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color ыкӏи уцышъо
  - theory3 2 тиориэ size
  - theory1 4 тиориэ size
  - predict yes if: theory3, shape ыкӏи sphere
  - predict yes if: theory0, color ыкӏи плъыжьы
  - predict yes if: theory1, color ыкӏи плъыжьы
  - predict yes if: theory1, color ыкӏи уцышъо
  - theory2 2 тиориэ size
  - theory0 4 тиориэ size
  - predict yes if: theory0, color ыкӏи blue
Cost model: exception cost 2.
Data:
  - item: i7, плъыжьы, sphere ыкӏи no
  - item: i2, плъыжьы, cube ыкӏи no
  - item: i5, плъыжьы, cone ыкӏи no
  - item: i6, плъыжьы, cube ыкӏи no
  - item: i3, уцышъо, cube ыкӏи yes
  - item: i1, blue, cone ыкӏи yes
  - item: i4, blue, cone ыкӏи no
  - item: i0, blue, cube ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - predict yes if: theory2, xim thiab ntsuab
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape thiab sphere
  - predict yes if: theory0, xim thiab liab
  - predict yes if: theory1, xim thiab liab
  - predict yes if: theory1, xim thiab ntsuab
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, xim thiab xiav
Cost model: exception nqi 2.
Data:
  - item: i7, liab, sphere thiab no
  - item: i2, liab, cube thiab no
  - item: i5, liab, cone thiab no
  - item: i6, liab, cube thiab no
  - item: i3, ntsuab, cube thiab yes
  - item: i1, xiav, cone thiab yes
  - item: i4, xiav, cone thiab no
  - item: i0, xiav, cube thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - predict ngai if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict ngai if: theory3, shape sphere
  - predict ngai if: theory0, color red
  - predict ngai if: theory1, color red
  - predict ngai if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ngai if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere woi
  - item: i2, red, cube woi
  - item: i5, red, cone woi
  - item: i6, red, cube woi
  - item: i3, green, cube ngai
  - item: i1, blue, cone ngai
  - item: i4, blue, cone woi
  - item: i0, blue, cube ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - predict i̠i if: theory2, color micha' green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict i̠i if: theory3, shape micha' sphere
  - predict i̠i if: theory0, color micha' homa
  - predict i̠i if: theory1, color micha' homa
  - predict i̠i if: theory1, color micha' green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict i̠i if: theory0, color micha' blue
Cost model: exception cost 2.
Data:
  - item: i7, homa, sphere micha' no
  - item: i2, homa, cube micha' no
  - item: i5, homa, cone micha' no
  - item: i6, homa, cube micha' no
  - item: i3, green, cube micha' i̠i
  - item: i1, blue, cone micha' i̠i
  - item: i4, blue, cone micha' no
  - item: i0, blue, cube micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - predict yes if: theory2, color カ green
  - theory3 2 theory size
  - theory1 4 theory size
  - predict yes if: theory3, shape カ sphere
  - predict yes if: theory0, color カ フレ
  - predict yes if: theory1, color カ フレ
  - predict yes if: theory1, color カ green
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, color カ blue
Cost model: exception cost 2.
Data:
  - item: i7, フレ, sphere カ ソモ
  - item: i2, フレ, cube カ ソモ
  - item: i5, フレ, cone カ ソモ
  - item: i6, フレ, cube カ ソモ
  - item: i3, green, cube カ yes
  - item: i1, blue, cone カ yes
  - item: i4, blue, cone カ ソモ
  - item: i0, blue, cube カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes эвыр: theory2, пэран ынкъам green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes эвыр: theory3, shape ынкъам sphere
  - predict yes эвыр: theory0, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory1, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory1, пэран ынкъам green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes эвыр: theory0, пэран ынкъам ныԓиԓиԓӄин
Cost model: exception cost 2.
Data:
  - item: i7, чеԓгыԓьын, sphere ынкъам no
  - item: i2, чеԓгыԓьын, cube ынкъам no
  - item: i5, чеԓгыԓьын, cone ынкъам no
  - item: i6, чеԓгыԓьын, cube ынкъам no
  - item: i3, green, cube ынкъам yes
  - item: i1, ныԓиԓиԓӄин, cone ынкъам yes
  - item: i4, ныԓиԓиԓӄин, cone ынкъам no
  - item: i0, ныԓиԓиԓӄин, cube ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes wa: theory2, color yéetel yaʼax
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes wa: theory3, shape yéetel sphere
  - predict yes wa: theory0, color yéetel chak
  - predict yes wa: theory1, color yéetel chak
  - predict yes wa: theory1, color yéetel yaʼax
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes wa: theory0, color yéetel blue
Cost model: exception cost 2.
Data:
  - item: i7, chak, sphere yéetel no
  - item: i2, chak, cube yéetel no
  - item: i5, chak, cone yéetel no
  - item: i6, chak, cube yéetel no
  - item: i3, yaʼax, cube yéetel yes
  - item: i1, blue, cone yéetel yes
  - item: i4, blue, cone yéetel no
  - item: i0, blue, cube yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict hą if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict hą if: theory3, shape sphere
  - predict hą if: theory0, color red
  - predict hą if: theory1, color red
  - predict hą if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict hą if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, green, cube hą
  - item: i1, blue, cone hą
  - item: i4, blue, cone no
  - item: i0, blue, cube hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - predict yes if: theory2, color na green
  - theory3 2 theory size
  - theory1 4 theory size
  - predict yes if: theory3, shape na sphere
  - predict yes if: theory0, color na red
  - predict yes if: theory1, color na red
  - predict yes if: theory1, color na green
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, color na blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere na no
  - item: i2, red, cube na no
  - item: i5, red, cone na no
  - item: i6, red, cube na no
  - item: i3, green, cube na yes
  - item: i1, blue, cone na yes
  - item: i4, blue, cone na no
  - item: i0, blue, cube na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict heẽ if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict heẽ if: theory3, shape sphere
  - predict heẽ if: theory0, color pytã
  - predict heẽ if: theory1, color pytã
  - predict heẽ if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict heẽ if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, pytã, sphere nahániri
  - item: i2, pytã, cube nahániri
  - item: i5, pytã, cone nahániri
  - item: i6, pytã, cube nahániri
  - item: i3, green, cube heẽ
  - item: i1, blue, cone heẽ
  - item: i4, blue, cone nahániri
  - item: i0, blue, cube heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict jisa if: theory2, sami ch’uxña
  - theory3 2 theory size
  - theory1 4 theory size
  - predict jisa if: theory3, shape sphere
  - predict jisa if: theory0, sami chupika
  - predict jisa if: theory1, sami chupika
  - predict jisa if: theory1, sami ch’uxña
  - theory2 2 theory size
  - theory0 4 theory size
  - predict jisa if: theory0, sami larama
Cost model: exception cost 2.
Data:
  - item: i7, chupika, sphere no
  - item: i2, chupika, cube no
  - item: i5, chupika, cone no
  - item: i6, chupika, cube no
  - item: i3, ch’uxña, cube jisa
  - item: i1, larama, cone jisa
  - item: i4, larama, cone no
  - item: i0, larama, cube jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ʻée if: theory2, color ǂʻá green
  - theory3 theory ʻǁnàhã 2
  - theory1 theory ʻǁnàhã 4
  - predict ʻée if: theory3, shape ǂʻá sphere
  - predict ʻée if: theory0, color ǂʻá red
  - predict ʻée if: theory1, color ǂʻá red
  - predict ʻée if: theory1, color ǂʻá green
  - theory2 theory ʻǁnàhã 2
  - theory0 theory ʻǁnàhã 4
  - predict ʻée if: theory0, color ǂʻá blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere ǂʻá ʻĩĩ
  - item: i2, red, cube ǂʻá ʻĩĩ
  - item: i5, red, cone ǂʻá ʻĩĩ
  - item: i6, red, cube ǂʻá ʻĩĩ
  - item: i3, green, cube ǂʻá ʻée
  - item: i1, blue, cone ǂʻá ʻée
  - item: i4, blue, cone ǂʻá ʻĩĩ
  - item: i0, blue, cube ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, вал green
  - theory3 2 theory size
  - theory1 4 theory size
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, вал red
  - predict yes if: theory1, вал red
  - predict yes if: theory1, вал green
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, вал blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, green, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone no
  - item: i0, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, color red
  - predict yes if: theory1, color red
  - predict yes if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, green, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone no
  - item: i0, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory3 2 theory size
  - theory1 4 theory size
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, color red
  - predict yes if: theory1, color red
  - predict yes if: theory1, color green
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, green, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone no
  - item: i0, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ee if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict ee if: theory3, shape sphere
  - predict ee if: theory0, color red
  - predict ee if: theory1, color red
  - predict ee if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ee if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, green, cube ee
  - item: i1, blue, cone ee
  - item: i4, blue, cone no
  - item: i0, blue, cube ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, color red
  - predict yes if: theory1, color red
  - predict yes if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere inte
  - item: i2, red, cube inte
  - item: i5, red, cone inte
  - item: i6, red, cube inte
  - item: i3, green, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone inte
  - item: i0, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - prédire oui si: theory2, couleur et vert
  - theory3 théorie taille 2
  - theory1 théorie taille 4
  - prédire oui si: theory3, forme et sphère
  - prédire oui si: theory0, couleur et rouge
  - prédire oui si: theory1, couleur et rouge
  - prédire oui si: theory1, couleur et vert
  - theory2 théorie taille 2
  - theory0 théorie taille 4
  - prédire oui si: theory0, couleur et bleu
Coûts : exception coûter 2.
Les données sont:
  - article: i7, rouge, sphère et no
  - article: i2, rouge, cube et no
  - article: i5, rouge, cône et no
  - article: i6, rouge, cube et no
  - article: i3, vert, cube et oui
  - article: i1, bleu, cône et oui
  - article: i4, bleu, cône et no
  - article: i0, bleu, cube et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - predict jes se: theory2, koloro kaj verda
  - theory3 teorio grandeco 2
  - theory1 teorio grandeco 4
  - predict jes se: theory3, formi kaj sfero
  - predict jes se: theory0, koloro kaj ruĝa
  - predict jes se: theory1, koloro kaj ruĝa
  - predict jes se: theory1, koloro kaj verda
  - theory2 teorio grandeco 2
  - theory0 teorio grandeco 4
  - predict jes se: theory0, koloro kaj blua
Kosto pozistino: escepto kosti 2.
Datumo:
  - ero: i7, ruĝa, sfero kaj no
  - ero: i2, ruĝa, kubo kaj no
  - ero: i5, ruĝa, strobilo kaj no
  - ero: i6, ruĝa, kubo kaj no
  - ero: i3, verda, kubo kaj jes
  - ero: i1, blua, strobilo kaj jes
  - ero: i4, blua, strobilo kaj no
  - ero: i0, blua, kubo kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - megjósol igen ha: theory2, szín és zöld
  - theory3 elmélet méret 2
  - theory1 elmélet méret 4
  - megjósol igen ha: theory3, alak és gömb
  - megjósol igen ha: theory0, szín és piros
  - megjósol igen ha: theory1, szín és piros
  - megjósol igen ha: theory1, szín és zöld
  - theory2 elmélet méret 2
  - theory0 elmélet méret 4
  - megjósol igen ha: theory0, szín és kék
Költség manöken: kivétel kerül 2.
Adat:
  - cikk: i7, piros, gömb és no
  - cikk: i2, piros, kocka és no
  - cikk: i5, piros, toboz és no
  - cikk: i6, piros, kocka és no
  - cikk: i3, zöld, kocka és igen
  - cikk: i1, kék, toboz és igen
  - cikk: i4, kék, toboz és no
  - cikk: i0, kék, kocka és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - predict係要是:theory2, 色及綠色嘅
  - theory3theory size2
  - theory1theory size4
  - predict係要是:theory3, 形狀及sphere
  - predict係要是:theory0, 色及紅
  - predict係要是:theory1, 色及紅
  - predict係要是:theory1, 色及綠色嘅
  - theory2theory size2
  - theory0theory size4
  - predict係要是:theory0, 色及藍色嘅
cost model:exception cost:2.
數據:
  - item:i7, 紅, sphere及no
  - item:i2, 紅, cube及no
  - item:i5, 紅, cone及no
  - item:i6, 紅, cube及no
  - item:i3, 綠色嘅, cube及係
  - item:i1, 藍色嘅, cone及係
  - item:i4, 藍色嘅, cone及no
  - item:i0, 藍色嘅, cube及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - predict yes if: theory2, color とぅ green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape とぅ sphere
  - predict yes if: theory0, color とぅ red
  - predict yes if: theory1, color とぅ red
  - predict yes if: theory1, color とぅ green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, color とぅ blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere とぅ no
  - item: i2, red, cube とぅ no
  - item: i5, red, cone とぅ no
  - item: i6, red, cube とぅ no
  - item: i3, green, cube とぅ yes
  - item: i1, blue, cone とぅ yes
  - item: i4, blue, cone とぅ no
  - item: i0, blue, cube とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - predict иә егер: theory2, түс және жасыл
  - theory3 теория мөлшер 2
  - theory1 теория мөлшер 4
  - predict иә егер: theory3, нысан және сфера
  - predict иә егер: theory0, түс және қызыл
  - predict иә егер: theory1, түс және қызыл
  - predict иә егер: theory1, түс және жасыл
  - theory2 теория мөлшер 2
  - theory0 теория мөлшер 4
  - predict иә егер: theory0, түс және көк
Тұру үлгі: ерекшелік тұру 2.
Деректер:
  - зат: i7, қызыл, сфера және жоқ
  - зат: i2, қызыл, куб және жоқ
  - зат: i5, қызыл, бүршік және жоқ
  - зат: i6, қызыл, куб және жоқ
  - зат: i3, жасыл, куб және иә
  - зат: i1, көк, бүршік және иә
  - зат: i4, көк, бүршік және жоқ
  - зат: i0, көк, куб және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - predict כֵּן אִם: theory2, צֶבַע וְ־ ירוק מקנאה
  - theory3 תּוֹרָה גֹּדֶל 2
  - theory1 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory3, צוּרָה וְ־ כַּדּוּר
  - predict כֵּן אִם: theory0, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory1, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory1, צֶבַע וְ־ ירוק מקנאה
  - theory2 תּוֹרָה גֹּדֶל 2
  - theory0 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory0, צֶבַע וְ־ כָּחוֹל
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 2.
נְתוּנִים:
  - אבזר: i7, אָדוֹם, כַּדּוּר וְ־ no
  - אבזר: i2, אָדוֹם, קובייה וְ־ no
  - אבזר: i5, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i6, אָדוֹם, קובייה וְ־ no
  - אבזר: i3, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i1, כָּחוֹל, אצטרובל וְ־ כֵּן
  - אבזר: i4, כָּחוֹל, אצטרובל וְ־ no
  - אבזר: i0, כָּחוֹל, קובייה וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - predict იახ if: theory2, color დო green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict იახ if: theory3, shape დო sphere
  - predict იახ if: theory0, color დო red
  - predict იახ if: theory1, color დო red
  - predict იახ if: theory1, color დო green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict იახ if: theory0, color დო blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere დო no
  - item: i2, red, cube დო no
  - item: i5, red, cone დო no
  - item: i6, red, cube დო no
  - item: i3, green, cube დო იახ
  - item: i1, blue, cone დო იახ
  - item: i4, blue, cone დო no
  - item: i0, blue, cube დო იახ
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - predict ya jika: theory2, warna dan hijau
  - theory3 teori ukuran 2
  - theory1 teori ukuran 4
  - predict ya jika: theory3, bentuk dan bola
  - predict ya jika: theory0, warna dan merah
  - predict ya jika: theory1, warna dan merah
  - predict ya jika: theory1, warna dan hijau
  - theory2 teori ukuran 2
  - theory0 teori ukuran 4
  - predict ya jika: theory0, warna dan biru
Cost model: pengecualian menghabiskan biaya 2.
Data:
  - benda: i7, merah, bola dan no
  - benda: i2, merah, kubus dan no
  - benda: i5, merah, kerucut dan no
  - benda: i6, merah, kubus dan no
  - benda: i3, hijau, kubus dan ya
  - benda: i1, biru, kerucut dan ya
  - benda: i4, biru, kerucut dan no
  - benda: i0, biru, kubus dan ya
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងបៃតង
  - theory3ទ្រឹស្ដីទំហំ2
  - theory1ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគោល
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងបៃតង
  - theory2ទ្រឹស្ដីទំហំ2
  - theory0ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងខៀវ
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:2.
ទិន្នន័យ:
  - item:i7, ក្រហម, គោលនឹងទេ
  - item:i2, ក្រហម, គូបនឹងទេ
  - item:i5, ក្រហម, កោណនឹងទេ
  - item:i6, ក្រហម, គូបនឹងទេ
  - item:i3, បៃតង, គូបនឹងច៎ា
  - item:i1, ខៀវ, កោណនឹងច៎ា
  - item:i4, ខៀវ, កោណនឹងទេ
  - item:i0, ខៀវ, គូបនឹងច៎ា
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຂຽວ
  - theory3ທິດສະດີຂະຫນາດ2
  - theory1ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະຄະດີ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຂຽວ
  - theory2ທິດສະດີຂະຫນາດ2
  - theory0ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຟ້າ
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:2.
ຂໍ້ມູນ:
  - item:i7, ແດງ, ຄະດີແລະno
  - item:i2, ແດງ, ລູກບາດແລະno
  - item:i5, ແດງ, coneແລະno
  - item:i6, ແດງ, ລູກບາດແລະno
  - item:i3, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i1, ຟ້າ, coneແລະແມ່ນ
  - item:i4, ຟ້າ, coneແລະno
  - item:i0, ຟ້າ, ລູກບາດແລະແມ່ນ
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - predict అవును అయితే: theory2, రంగు మరియు పచ్చని
  - theory3 2 theory size
  - theory1 4 theory size
  - predict అవును అయితే: theory3, ఆకారం మరియు గోళము
  - predict అవును అయితే: theory0, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory1, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory1, రంగు మరియు పచ్చని
  - theory2 2 theory size
  - theory0 4 theory size
  - predict అవును అయితే: theory0, రంగు మరియు నీలము
cost model: exception cost 2.
data:
  - item: i7, ఎర్ర, గోళము మరియు no
  - item: i2, ఎర్ర, cube మరియు no
  - item: i5, ఎర్ర, cone మరియు no
  - item: i6, ఎర్ర, cube మరియు no
  - item: i3, పచ్చని, cube మరియు అవును
  - item: i1, నీలము, cone మరియు అవును
  - item: i4, నీలము, cone మరియు no
  - item: i0, నీలము, cube మరియు అవును
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - predict hẹn if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict hẹn if: theory3, shape sphere
  - predict hẹn if: theory0, color pupa
  - predict hẹn if: theory1, color pupa
  - predict hẹn if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict hẹn if: theory0, color dúdú
Cost model: exception cost 2.
Data:
  - item: i7, pupa, sphere rárá
  - item: i2, pupa, cube rárá
  - item: i5, pupa, cone rárá
  - item: i6, pupa, cube rárá
  - item: i3, green, cube bẹ́ẹ̀ ni
  - item: i1, dúdú, cone bẹ́ẹ̀ ni
  - item: i4, dúdú, cone rárá
  - item: i0, dúdú, cube bẹ́ẹ̀ ni
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict au if: theory2, color green
  - theory3 2 theory size
  - theory1 4 theory size
  - predict au if: theory3, shape sphere
  - predict au if: theory0, color łichíí
  - predict au if: theory1, color łichíí
  - predict au if: theory1, color green
  - theory2 2 theory size
  - theory0 4 theory size
  - predict au if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, łichíí, sphere no
  - item: i2, łichíí, cube no
  - item: i5, łichíí, cone no
  - item: i6, łichíí, cube no
  - item: i3, green, cube au
  - item: i1, blue, cone au
  - item: i4, blue, cone no
  - item: i0, blue, cube au
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - predict yes if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, color red
  - predict yes if: theory1, color red
  - predict yes if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, color blue
cost model: exception cost 2.
data:
  - item: i7, red, sphere ᐋᒃᑲ
  - item: i2, red, cube ᐋᒃᑲ
  - item: i5, red, cone ᐋᒃᑲ
  - item: i6, red, cube ᐋᒃᑲ
  - item: i3, green, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone ᐋᒃᑲ
  - item: i0, blue, cube yes
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color grun
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, color red
  - predict yes if: theory1, color red
  - predict yes if: theory1, color grun
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, color blaw
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, grun, cube yes
  - item: i1, blaw, cone yes
  - item: i4, blaw, cone no
  - item: i0, blaw, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color green
  - theory3 theory size 2
  - theory1 theory size 4
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, color red
  - predict yes if: theory1, color red
  - predict yes if: theory1, color green
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, color blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, green, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone no
  - item: i0, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, ранг къацу
  - theory3 2 theory size
  - theory1 4 theory size
  - predict yes if: theory3, shape sphere
  - predict yes if: theory0, ранг red
  - predict yes if: theory1, ранг red
  - predict yes if: theory1, ранг къацу
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, ранг blue
Cost model: exception cost 2.
Data:
  - item: i7, red, sphere no
  - item: i2, red, cube no
  - item: i5, red, cone no
  - item: i6, red, cube no
  - item: i3, къацу, cube yes
  - item: i1, blue, cone yes
  - item: i4, blue, cone no
  - item: i0, blue, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - predicts yes if: theory2, colour and blue
  - theory1 theory size 2
  - predicts yes if: theory1, shape and cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predicts yes if: theory0, colour and blue
  - predicts yes if: theory3, shape and cone
  - predicts yes if: theory0, colour and green
  - predicts yes if: theory3, colour and blue
Costs: exception cost 3.
The data are:
  - item: i7, blue, cone and no
  - item: i1, red, cone and yes
  - item: i4, red, sphere and yes
  - item: i5, green, cube and yes
  - item: i6, red, cone and no
  - item: i2, red, cone and no
  - item: i3, red, cone and no
  - item: i0, green, cone and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - predice sí si: theory2, es de color y azul
  - theory1 el tamaño de la teoría 2
  - predice sí si: theory1, forma y cono
  - theory2 el tamaño de la teoría 2
  - theory3 el tamaño de la teoría 4
  - theory0 el tamaño de la teoría 4
  - predice sí si: theory0, es de color y azul
  - predice sí si: theory3, forma y cono
  - predice sí si: theory0, es de color y verde
  - predice sí si: theory3, es de color y azul
Costes: exception el coste: 3.
Los datos son:
  - el elemento: i7, azul, cono y no
  - el elemento: i1, rojo, cono y sí
  - el elemento: i4, rojo, esfera y sí
  - el elemento: i5, verde, cubo y sí
  - el elemento: i6, rojo, cono y no
  - el elemento: i2, rojo, cono y no
  - el elemento: i3, rojo, cono y no
  - el elemento: i0, verde, cono y no
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - 预测是若：theory2、颜色和蓝色的
  - theory1的理论规模是2
  - 预测是若：theory1、形状和圆锥
  - theory2的理论规模是2
  - theory3的理论规模是4
  - theory0的理论规模是4
  - 预测是若：theory0、颜色和蓝色的
  - 预测是若：theory3、形状和圆锥
  - 预测是若：theory0、颜色和绿色的
  - 预测是若：theory3、颜色和蓝色的
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i7、蓝色的、圆锥和否
  - 的物品是：i1、红色的、圆锥和是
  - 的物品是：i4、红色的、球体和是
  - 的物品是：i5、绿色的、立方体和是
  - 的物品是：i6、红色的、圆锥和否
  - 的物品是：i2、红色的、圆锥和否
  - 的物品是：i3、红色的、圆锥和否
  - 的物品是：i0、绿色的、圆锥和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - öngörür evet eğer: theory2, renk ve mavi
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory1, şekil ve koni
  - theory2 2 kuram boyut
  - theory3 4 kuram boyut
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, renk ve mavi
  - öngörür evet eğer: theory3, şekil ve koni
  - öngörür evet eğer: theory0, renk ve yeşil
  - öngörür evet eğer: theory3, renk ve mavi
Cost model: exception maliyet: 3.
Veriler:
  - item: i7, mavi, koni ve hayır
  - item: i1, kırmızı, koni ve evet
  - item: i4, kırmızı, küre ve evet
  - item: i5, yeşil, küp ve evet
  - item: i6, kırmızı, koni ve hayır
  - item: i2, kırmızı, koni ve hayır
  - item: i3, kırmızı, koni ve hayır
  - item: i0, yeşil, koni ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na buluu
  - theory1 theory size 2
  - predict ndiyo kama: theory1, umbo na koni
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict ndiyo kama: theory0, rangi na buluu
  - predict ndiyo kama: theory3, umbo na koni
  - predict ndiyo kama: theory0, rangi na kijani
  - predict ndiyo kama: theory3, rangi na buluu
Cost model: exception gharimu: 3.
Data:
  - item: i7, buluu, koni na hapana
  - item: i1, ekundu, koni na ndiyo
  - item: i4, ekundu, tufe na ndiyo
  - item: i5, kijani, mchemraba na ndiyo
  - item: i6, ekundu, koni na hapana
  - item: i2, ekundu, koni na hapana
  - item: i3, ekundu, koni na hapana
  - item: i0, kijani, koni na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - predicts yes if: theory2, colour and blue
  - theory1 theory size 2
  - predicts yes if: theory1, shape and cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predicts yes if: theory0, colour and blue
  - predicts yes if: theory3, shape and cone
  - predicts yes if: theory0, colour and green
  - predicts yes if: theory3, colour and blue
Costs: exception cost 3.
The data are:
  - item: i7, blue, cone and no
  - item: i1, red, cone and yes
  - item: i4, red, sphere and yes
  - item: i5, green, cube and yes
  - item: i6, red, cone and no
  - item: i2, red, cone and no
  - item: i3, red, cone and no
  - item: i0, green, cone and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(predicts_yes_if theory2 color blue) (theory_size theory1 2) (predicts_yes_if theory1 shape cone) (theory_size theory2 2) (theory_size theory3 4) (theory_size theory0 4) (predicts_yes_if theory0 color blue) (predicts_yes_if theory3 shape cone) (predicts_yes_if theory0 color green) (predicts_yes_if theory3 color blue)], cost_model: [(exception_cost 3)], data: [(item i7 blue cone no) (item i1 red cone yes) (item i4 red sphere yes) (item i5 green cube yes) (item i6 red cone no) (item i2 red cone no) (item i3 red cone no) (item i0 green cone no)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - vorhersagen ja wenn: theory2, Farbe und blau
  - theory1 Theorie Größe 2
  - vorhersagen ja wenn: theory1, Form und Kegel
  - theory2 Theorie Größe 2
  - theory3 Theorie Größe 4
  - theory0 Theorie Größe 4
  - vorhersagen ja wenn: theory0, Farbe und blau
  - vorhersagen ja wenn: theory3, Form und Kegel
  - vorhersagen ja wenn: theory0, Farbe und grün
  - vorhersagen ja wenn: theory3, Farbe und blau
Kosten: Ausnahme kosten 3.
Die Daten sind:
  - Ding: i7, blau, Kegel und nein
  - Ding: i1, rot, Kegel und ja
  - Ding: i4, rot, Kugel und ja
  - Ding: i5, grün, Kubus und ja
  - Ding: i6, rot, Kegel und nein
  - Ding: i2, rot, Kegel und nein
  - Ding: i3, rot, Kegel und nein
  - Ding: i0, grün, Kegel und nein
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - predvidjeti да ȁko: theory2, бо̀ја и пла̑в
  - theory1 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory1, о̏блӣк и stožac
  - theory2 тѐо̄рија величѝна 2
  - theory3 тѐо̄рија величѝна 4
  - theory0 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory0, бо̀ја и пла̑в
  - predvidjeti да ȁko: theory3, о̏блӣк и stožac
  - predvidjeti да ȁko: theory0, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory3, бо̀ја и пла̑в
Trošak мо̀дел: изузе́так ко̏штати 3.
Податци:
  - item: i7, пла̑в, stožac и no
  - item: i1, цр̀вен, stožac и да
  - item: i4, цр̀вен, сфе́ра и да
  - item: i5, зѐлен, коцка и да
  - item: i6, цр̀вен, stožac и no
  - item: i2, цр̀вен, stožac и no
  - item: i3, цр̀вен, stožac и no
  - item: i0, зѐлен, stožac и no
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - ennakoida kyllä jos: theory2, väri ja sininen
  - theory1 teoria koko 2
  - ennakoida kyllä jos: theory1, muoto ja kartio
  - theory2 teoria koko 2
  - theory3 teoria koko 4
  - theory0 teoria koko 4
  - ennakoida kyllä jos: theory0, väri ja sininen
  - ennakoida kyllä jos: theory3, muoto ja kartio
  - ennakoida kyllä jos: theory0, väri ja vihreä
  - ennakoida kyllä jos: theory3, väri ja sininen
Kustannus malli: poikkeus maksaa 3.
Tieto:
  - esine: i7, sininen, kartio ja ei
  - esine: i1, punainen, kartio ja kyllä
  - esine: i4, punainen, pallo ja kyllä
  - esine: i5, vihreä, kuutio ja kyllä
  - esine: i6, punainen, kartio ja ei
  - esine: i2, punainen, kartio ja ei
  - esine: i3, punainen, kartio ja ei
  - esine: i0, vihreä, kartio ja ei
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - 预测是若：theory2、颜色和蓝色的
  - theory1的理论规模是2
  - 预测是若：theory1、形状和圆锥
  - theory2的理论规模是2
  - theory3的理论规模是4
  - theory0的理论规模是4
  - 预测是若：theory0、颜色和蓝色的
  - 预测是若：theory3、形状和圆锥
  - 预测是若：theory0、颜色和绿色的
  - 预测是若：theory3、颜色和蓝色的
代价模型如下：exception的成本：3。
数据如下：
  - 的物品是：i7、蓝色的、圆锥和否
  - 的物品是：i1、红色的、圆锥和是
  - 的物品是：i4、红色的、球体和是
  - 的物品是：i5、绿色的、立方体和是
  - 的物品是：i6、红色的、圆锥和否
  - 的物品是：i2、红色的、圆锥和否
  - 的物品是：i3、红色的、圆锥和否
  - 的物品是：i0、绿色的、圆锥和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - 予言するはいもし...:theory2, 色と青い
  - theory12理論大きさ
  - 予言するはいもし...:theory1, 形と松毬
  - theory22理論大きさ
  - theory34理論大きさ
  - theory04理論大きさ
  - 予言するはいもし...:theory0, 色と青い
  - 予言するはいもし...:theory3, 形と松毬
  - 予言するはいもし...:theory0, 色と緑の
  - 予言するはいもし...:theory3, 色と青い
費用モデル:例外費やす:3.
資料:
  - 項目:i7, 青い, 松毬といいえ
  - 項目:i1, 赤い, 松毬とはい
  - 項目:i4, 赤い, 球とはい
  - 項目:i5, 緑の, 立方体とはい
  - 項目:i6, 赤い, 松毬といいえ
  - 項目:i2, 赤い, 松毬といいえ
  - 項目:i3, 赤い, 松毬といいえ
  - 項目:i0, 緑の, 松毬といいえ
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - öngörür evet eğer: theory2, renk ve mavi
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory1, şekil ve koni
  - theory2 2 kuram boyut
  - theory3 4 kuram boyut
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, renk ve mavi
  - öngörür evet eğer: theory3, şekil ve koni
  - öngörür evet eğer: theory0, renk ve yeşil
  - öngörür evet eğer: theory3, renk ve mavi
Cost model: exception maliyet: 3.
Veriler:
  - item: i7, mavi, koni ve hayır
  - item: i1, kırmızı, koni ve evet
  - item: i4, kırmızı, küre ve evet
  - item: i5, yeşil, küp ve evet
  - item: i6, kırmızı, koni ve hayır
  - item: i2, kırmızı, koni ve hayır
  - item: i3, kırmızı, koni ve hayır
  - item: i0, yeşil, koni ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَزْرَق
  - نَظَرِيَّة مَقَاس theory1 2
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ مَخْرُوط
  - نَظَرِيَّة مَقَاس theory2 2
  - نَظَرِيَّة مَقَاس theory3 4
  - نَظَرِيَّة مَقَاس theory0 4
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَزْرَق
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَزْرَق
cost model: اِسْتِثْنَاء cost 3.
بَيَانَات:
  - بَنْد: i7، أَزْرَق، مَخْرُوط وَـ no
  - بَنْد: i1، أَحْمَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i4، أَحْمَر، كُرَة وَـ نَعَم
  - بَنْد: i5، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i6، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i2، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i3، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i0، أَخْضَر، مَخْرُوط وَـ no
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - predict დიახ თუ: theory2, ფერი და ლურჯი
  - theory1 2 თეორია ზომა
  - predict დიახ თუ: theory1, ფორმა და გირჩა
  - theory2 2 თეორია ზომა
  - theory3 4 თეორია ზომა
  - theory0 4 თეორია ზომა
  - predict დიახ თუ: theory0, ფერი და ლურჯი
  - predict დიახ თუ: theory3, ფორმა და გირჩა
  - predict დიახ თუ: theory0, ფერი და მწვანე
  - predict დიახ თუ: theory3, ფერი და ლურჯი
Ფასი მოდელი: გამონაკლისი ღირს 3.
Მონაცემები:
  - item: i7, ლურჯი, გირჩა და no
  - item: i1, წითელი, გირჩა და დიახ
  - item: i4, წითელი, სფერო და დიახ
  - item: i5, მწვანე, კუბი და დიახ
  - item: i6, წითელი, გირჩა და no
  - item: i2, წითელი, გირჩა და no
  - item: i3, წითელი, გირჩა და no
  - item: i0, მწვანე, გირჩა და no
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - matakite āe mēnā: theory2, kano me kahurangi
  - ariā size theory1 2
  - matakite āe mēnā: theory1, āhuahanga me koeko
  - ariā size theory2 2
  - ariā size theory3 4
  - ariā size theory0 4
  - matakite āe mēnā: theory0, kano me kahurangi
  - matakite āe mēnā: theory3, āhuahanga me koeko
  - matakite āe mēnā: theory0, kano me kākāriki
  - matakite āe mēnā: theory3, kano me kahurangi
Cost model: hāungatanga cost 3.
Hōtuku:
  - tūemi: i7, kahurangi, koeko me kāo
  - tūemi: i1, red, koeko me āe
  - tūemi: i4, red, poi me āe
  - tūemi: i5, kākāriki, mataono me āe
  - tūemi: i6, red, koeko me kāo
  - tūemi: i2, red, koeko me kāo
  - tūemi: i3, red, koeko me kāo
  - tūemi: i0, kākāriki, koeko me kāo
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - dự báo vâng nếu: theory2, màu và blue
  - theory1 lý thuyết kích thước 2
  - dự báo vâng nếu: theory1, hình dáng và nón
  - theory2 lý thuyết kích thước 2
  - theory3 lý thuyết kích thước 4
  - theory0 lý thuyết kích thước 4
  - dự báo vâng nếu: theory0, màu và blue
  - dự báo vâng nếu: theory3, hình dáng và nón
  - dự báo vâng nếu: theory0, màu và green
  - dự báo vâng nếu: theory3, màu và blue
Cost model: lệ ngoại cost 3.
Dữ liệu:
  - vật phẩm: i7, blue, nón và no
  - vật phẩm: i1, đỏ, nón và vâng
  - vật phẩm: i4, đỏ, mặt cầu và vâng
  - vật phẩm: i5, green, lập phương và vâng
  - vật phẩm: i6, đỏ, nón và no
  - vật phẩm: i2, đỏ, nón và no
  - vật phẩm: i3, đỏ, nón và no
  - vật phẩm: i0, green, nón và no
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - ทำนายใช่ถ้า หาก:theory2, สีและน้ำเงิน
  - theory1ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและกรวย
  - theory2ทฤษฎีขนาด2
  - theory3ทฤษฎีขนาด4
  - theory0ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory0, สีและน้ำเงิน
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory0, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory3, สีและน้ำเงิน
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:3.
ข้อมูล:
  - item:i7, น้ำเงิน, กรวยและไฮ้
  - item:i1, แดง, กรวยและใช่
  - item:i4, แดง, ทรงกลมและใช่
  - item:i5, เขียว, ลูกบาศก์และใช่
  - item:i6, แดง, กรวยและไฮ้
  - item:i2, แดง, กรวยและไฮ้
  - item:i3, แดง, กรวยและไฮ้
  - item:i0, เขียว, กรวยและไฮ้
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் நீலம்
  - theory1 2 theory size
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் நீலம்
  - கணி ஆம் என்றால்: theory3, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் நீலம்
cost model: exception செலவு 3.
தரவுகள்:
  - item: i7, நீலம், cone மற்றும் no
  - item: i1, சிவப்பு, cone மற்றும் ஆம்
  - item: i4, சிவப்பு, sphere மற்றும் ஆம்
  - item: i5, பச்சை, cube மற்றும் ஆம்
  - item: i6, சிவப்பு, cone மற்றும் no
  - item: i2, சிவப்பு, cone மற்றும் no
  - item: i3, சிவப்பு, cone மற்றும் no
  - item: i0, பச்சை, cone மற்றும் no
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - predict ndiyo kama: theory2, rangi na buluu
  - theory1 theory size 2
  - predict ndiyo kama: theory1, umbo na koni
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict ndiyo kama: theory0, rangi na buluu
  - predict ndiyo kama: theory3, umbo na koni
  - predict ndiyo kama: theory0, rangi na kijani
  - predict ndiyo kama: theory3, rangi na buluu
Cost model: exception gharimu: 3.
Data:
  - item: i7, buluu, koni na hapana
  - item: i1, ekundu, koni na ndiyo
  - item: i4, ekundu, tufe na ndiyo
  - item: i5, kijani, mchemraba na ndiyo
  - item: i6, ekundu, koni na hapana
  - item: i2, ekundu, koni na hapana
  - item: i3, ekundu, koni na hapana
  - item: i0, kijani, koni na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dootłʼizh
  - theory1 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó dootłʼizh
Cost model: exception bą́ą́h ílį́ 3.
Data:
  - item: i7, dootłʼizh, cone dóó no
  - item: i1, łichííʼ, cone dóó aooʼ
  - item: i4, łichííʼ, sphere dóó aooʼ
  - item: i5, dinootłʼizh, cube dóó aooʼ
  - item: i6, łichííʼ, cone dóó no
  - item: i2, łichííʼ, cone dóó no
  - item: i3, łichííʼ, cone dóó no
  - item: i0, dinootłʼizh, cone dóó no
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - predict aap if: theory2, qalipaat aamma tungujortoq
  - theory1 2 theory size
  - predict aap if: theory1, shape aamma cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict aap if: theory0, qalipaat aamma tungujortoq
  - predict aap if: theory3, shape aamma cone
  - predict aap if: theory0, qalipaat aamma qorsuk
  - predict aap if: theory3, qalipaat aamma tungujortoq
Cost model: exception cost 3.
Data:
  - item: i7, tungujortoq, cone aamma no
  - item: i1, aappalajaarpoq, cone aamma aap
  - item: i4, aappalajaarpoq, sphere aamma aap
  - item: i5, qorsuk, cube aamma aap
  - item: i6, aappalajaarpoq, cone aamma no
  - item: i2, aappalajaarpoq, cone aamma no
  - item: i3, aappalajaarpoq, cone aamma no
  - item: i0, qorsuk, cone aamma no
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ya sapos: theory2, kala na blu
  - theory1 theory size 2
  - predict ya sapos: theory1, shape na cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict ya sapos: theory0, kala na blu
  - predict ya sapos: theory3, shape na cone
  - predict ya sapos: theory0, kala na grinpela
  - predict ya sapos: theory3, kala na blu
Cost model: exception cost 3.
Data:
  - item: i7, blu, cone na no
  - item: i1, ret, cone na ya
  - item: i4, ret, sphere na ya
  - item: i5, grinpela, cube na ya
  - item: i6, ret, cone na no
  - item: i2, ret, cone na no
  - item: i3, ret, cone na no
  - item: i0, grinpela, cone na no
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color blue
  - theory1 theory size 2
  - predict yes if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color green
  - predict yes if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, màxke, cone yes
  - item: i4, màxke, sphere yes
  - item: i5, green, cube yes
  - item: i6, màxke, cone no
  - item: i2, màxke, cone no
  - item: i3, màxke, cone no
  - item: i0, green, cone no
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - predict yes if: theory2, бос а blue
  - theory1 theory size 2
  - predict yes if: theory1, shape а cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, бос а blue
  - predict yes if: theory3, shape а cone
  - predict yes if: theory0, бос а баьццара
  - predict yes if: theory3, бос а blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone а no
  - item: i1, цӏе, cone а yes
  - item: i4, цӏе, sphere а yes
  - item: i5, баьццара, cube а yes
  - item: i6, цӏе, cone а no
  - item: i2, цӏе, cone а no
  - item: i3, цӏе, cone а no
  - item: i0, баьццара, cone а no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - 예언하다 네 만약: theory2, 색깔 푸르다
  - theory1 2 이론 크기
  - 예언하다 네 만약: theory1, 모양 솔방울
  - theory2 2 이론 크기
  - theory3 4 이론 크기
  - theory0 4 이론 크기
  - 예언하다 네 만약: theory0, 색깔 푸르다
  - 예언하다 네 만약: theory3, 모양 솔방울
  - 예언하다 네 만약: theory0, 색깔 녹색의
  - 예언하다 네 만약: theory3, 색깔 푸르다
cost model: 예외 cost 3.
자료:
  - 아이템: i7, 푸르다, 솔방울 아니요
  - 아이템: i1, 빨갛다, 솔방울 네
  - 아이템: i4, 빨갛다, 구체 네
  - 아이템: i5, 녹색의, 정육면체 네
  - 아이템: i6, 빨갛다, 솔방울 아니요
  - 아이템: i2, 빨갛다, 솔방울 아니요
  - 아이템: i3, 빨갛다, 솔방울 아니요
  - 아이템: i0, 녹색의, 솔방울 아니요
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - aurresan bai if: theory2, kolore eta urdin
  - theory1 2 theory tamaina
  - aurresan bai if: theory1, shape eta pinaburu
  - theory2 2 theory tamaina
  - theory3 4 theory tamaina
  - theory0 4 theory tamaina
  - aurresan bai if: theory0, kolore eta urdin
  - aurresan bai if: theory3, shape eta pinaburu
  - aurresan bai if: theory0, kolore eta berde
  - aurresan bai if: theory3, kolore eta urdin
Cost model: exception kostatu 3.
Data:
  - item: i7, urdin, pinaburu eta no
  - item: i1, gorri, pinaburu eta bai
  - item: i4, gorri, esfera eta bai
  - item: i5, berde, kubo eta bai
  - item: i6, gorri, pinaburu eta no
  - item: i2, gorri, pinaburu eta no
  - item: i3, gorri, pinaburu eta no
  - item: i0, berde, pinaburu eta no
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - theory1 theory size 2
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - predict ᎥᎥ if: theory3, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
Cost model: exception cost 3.
Data:
  - item: i7, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᏝᏃ
  - item: i1, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
  - item: i4, ᎩᎦᎨ, sphere ᎠᎴ ᎥᎥ
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i6, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i2, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i3, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i0, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᏝᏃ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict arí if: theory2, llimphi anqas
  - theory1 theory chhika 2
  - predict arí if: theory1, shape cone
  - theory2 theory chhika 2
  - theory3 theory chhika 4
  - theory0 theory chhika 4
  - predict arí if: theory0, llimphi anqas
  - predict arí if: theory3, shape cone
  - predict arí if: theory0, llimphi waylla
  - predict arí if: theory3, llimphi anqas
Cost model: exception cost 3.
Data:
  - item: i7, anqas, cone mánan
  - item: i1, puka, cone arí
  - item: i4, puka, sphere arí
  - item: i5, waylla, cube arí
  - item: i6, puka, cone mánan
  - item: i2, puka, cone mánan
  - item: i3, puka, cone mánan
  - item: i0, waylla, cone mánan
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color ыкӏи blue
  - theory1 2 тиориэ size
  - predict yes if: theory1, shape ыкӏи cone
  - theory2 2 тиориэ size
  - theory3 4 тиориэ size
  - theory0 4 тиориэ size
  - predict yes if: theory0, color ыкӏи blue
  - predict yes if: theory3, shape ыкӏи cone
  - predict yes if: theory0, color ыкӏи уцышъо
  - predict yes if: theory3, color ыкӏи blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone ыкӏи no
  - item: i1, плъыжьы, cone ыкӏи yes
  - item: i4, плъыжьы, sphere ыкӏи yes
  - item: i5, уцышъо, cube ыкӏи yes
  - item: i6, плъыжьы, cone ыкӏи no
  - item: i2, плъыжьы, cone ыкӏи no
  - item: i3, плъыжьы, cone ыкӏи no
  - item: i0, уцышъо, cone ыкӏи no
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - predict yes if: theory2, xim thiab xiav
  - theory1 theory size 2
  - predict yes if: theory1, shape thiab cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, xim thiab xiav
  - predict yes if: theory3, shape thiab cone
  - predict yes if: theory0, xim thiab ntsuab
  - predict yes if: theory3, xim thiab xiav
Cost model: exception nqi 3.
Data:
  - item: i7, xiav, cone thiab no
  - item: i1, liab, cone thiab yes
  - item: i4, liab, sphere thiab yes
  - item: i5, ntsuab, cube thiab yes
  - item: i6, liab, cone thiab no
  - item: i2, liab, cone thiab no
  - item: i3, liab, cone thiab no
  - item: i0, ntsuab, cone thiab no
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - predict ngai if: theory2, color blue
  - theory1 theory size 2
  - predict ngai if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict ngai if: theory0, color blue
  - predict ngai if: theory3, shape cone
  - predict ngai if: theory0, color green
  - predict ngai if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone woi
  - item: i1, red, cone ngai
  - item: i4, red, sphere ngai
  - item: i5, green, cube ngai
  - item: i6, red, cone woi
  - item: i2, red, cone woi
  - item: i3, red, cone woi
  - item: i0, green, cone woi
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - predict i̠i if: theory2, color micha' blue
  - theory1 theory size 2
  - predict i̠i if: theory1, shape micha' cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict i̠i if: theory0, color micha' blue
  - predict i̠i if: theory3, shape micha' cone
  - predict i̠i if: theory0, color micha' green
  - predict i̠i if: theory3, color micha' blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone micha' no
  - item: i1, homa, cone micha' i̠i
  - item: i4, homa, sphere micha' i̠i
  - item: i5, green, cube micha' i̠i
  - item: i6, homa, cone micha' no
  - item: i2, homa, cone micha' no
  - item: i3, homa, cone micha' no
  - item: i0, green, cone micha' no
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - predict yes if: theory2, color カ blue
  - theory1 2 theory size
  - predict yes if: theory1, shape カ cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict yes if: theory0, color カ blue
  - predict yes if: theory3, shape カ cone
  - predict yes if: theory0, color カ green
  - predict yes if: theory3, color カ blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone カ ソモ
  - item: i1, フレ, cone カ yes
  - item: i4, フレ, sphere カ yes
  - item: i5, green, cube カ yes
  - item: i6, フレ, cone カ ソモ
  - item: i2, フレ, cone カ ソモ
  - item: i3, フレ, cone カ ソモ
  - item: i0, green, cone カ ソモ
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes эвыр: theory2, пэран ынкъам ныԓиԓиԓӄин
  - theory1 theory size 2
  - predict yes эвыр: theory1, shape ынкъам cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes эвыр: theory0, пэран ынкъам ныԓиԓиԓӄин
  - predict yes эвыр: theory3, shape ынкъам cone
  - predict yes эвыр: theory0, пэран ынкъам green
  - predict yes эвыр: theory3, пэран ынкъам ныԓиԓиԓӄин
Cost model: exception cost 3.
Data:
  - item: i7, ныԓиԓиԓӄин, cone ынкъам no
  - item: i1, чеԓгыԓьын, cone ынкъам yes
  - item: i4, чеԓгыԓьын, sphere ынкъам yes
  - item: i5, green, cube ынкъам yes
  - item: i6, чеԓгыԓьын, cone ынкъам no
  - item: i2, чеԓгыԓьын, cone ынкъам no
  - item: i3, чеԓгыԓьын, cone ынкъам no
  - item: i0, green, cone ынкъам no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes wa: theory2, color yéetel blue
  - theory1 theory size 2
  - predict yes wa: theory1, shape yéetel cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes wa: theory0, color yéetel blue
  - predict yes wa: theory3, shape yéetel cone
  - predict yes wa: theory0, color yéetel yaʼax
  - predict yes wa: theory3, color yéetel blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone yéetel no
  - item: i1, chak, cone yéetel yes
  - item: i4, chak, sphere yéetel yes
  - item: i5, yaʼax, cube yéetel yes
  - item: i6, chak, cone yéetel no
  - item: i2, chak, cone yéetel no
  - item: i3, chak, cone yéetel no
  - item: i0, yaʼax, cone yéetel no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict hą if: theory2, color blue
  - theory1 theory size 2
  - predict hą if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict hą if: theory0, color blue
  - predict hą if: theory3, shape cone
  - predict hą if: theory0, color green
  - predict hą if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, red, cone hą
  - item: i4, red, sphere hą
  - item: i5, green, cube hą
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, green, cone no
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - predict yes if: theory2, color na blue
  - theory1 2 theory size
  - predict yes if: theory1, shape na cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict yes if: theory0, color na blue
  - predict yes if: theory3, shape na cone
  - predict yes if: theory0, color na green
  - predict yes if: theory3, color na blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone na no
  - item: i1, red, cone na yes
  - item: i4, red, sphere na yes
  - item: i5, green, cube na yes
  - item: i6, red, cone na no
  - item: i2, red, cone na no
  - item: i3, red, cone na no
  - item: i0, green, cone na no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict heẽ if: theory2, color blue
  - theory1 theory size 2
  - predict heẽ if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict heẽ if: theory0, color blue
  - predict heẽ if: theory3, shape cone
  - predict heẽ if: theory0, color green
  - predict heẽ if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone nahániri
  - item: i1, pytã, cone heẽ
  - item: i4, pytã, sphere heẽ
  - item: i5, green, cube heẽ
  - item: i6, pytã, cone nahániri
  - item: i2, pytã, cone nahániri
  - item: i3, pytã, cone nahániri
  - item: i0, green, cone nahániri
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict jisa if: theory2, sami larama
  - theory1 2 theory size
  - predict jisa if: theory1, shape cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict jisa if: theory0, sami larama
  - predict jisa if: theory3, shape cone
  - predict jisa if: theory0, sami ch’uxña
  - predict jisa if: theory3, sami larama
Cost model: exception cost 3.
Data:
  - item: i7, larama, cone no
  - item: i1, chupika, cone jisa
  - item: i4, chupika, sphere jisa
  - item: i5, ch’uxña, cube jisa
  - item: i6, chupika, cone no
  - item: i2, chupika, cone no
  - item: i3, chupika, cone no
  - item: i0, ch’uxña, cone no
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ʻée if: theory2, color ǂʻá blue
  - theory1 theory ʻǁnàhã 2
  - predict ʻée if: theory1, shape ǂʻá cone
  - theory2 theory ʻǁnàhã 2
  - theory3 theory ʻǁnàhã 4
  - theory0 theory ʻǁnàhã 4
  - predict ʻée if: theory0, color ǂʻá blue
  - predict ʻée if: theory3, shape ǂʻá cone
  - predict ʻée if: theory0, color ǂʻá green
  - predict ʻée if: theory3, color ǂʻá blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone ǂʻá ʻĩĩ
  - item: i1, red, cone ǂʻá ʻée
  - item: i4, red, sphere ǂʻá ʻée
  - item: i5, green, cube ǂʻá ʻée
  - item: i6, red, cone ǂʻá ʻĩĩ
  - item: i2, red, cone ǂʻá ʻĩĩ
  - item: i3, red, cone ǂʻá ʻĩĩ
  - item: i0, green, cone ǂʻá ʻĩĩ
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, вал blue
  - theory1 2 theory size
  - predict yes if: theory1, shape cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict yes if: theory0, вал blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, вал green
  - predict yes if: theory3, вал blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, green, cube yes
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, green, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color blue
  - theory1 theory size 2
  - predict yes if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color green
  - predict yes if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, green, cube yes
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, green, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color blue
  - theory1 2 theory size
  - predict yes if: theory1, shape cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict yes if: theory0, color blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color green
  - predict yes if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, green, cube yes
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, green, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ee if: theory2, color blue
  - theory1 theory size 2
  - predict ee if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict ee if: theory0, color blue
  - predict ee if: theory3, shape cone
  - predict ee if: theory0, color green
  - predict ee if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, red, cone ee
  - item: i4, red, sphere ee
  - item: i5, green, cube ee
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, green, cone no
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color blue
  - theory1 theory size 2
  - predict yes if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color green
  - predict yes if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone inte
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, green, cube yes
  - item: i6, red, cone inte
  - item: i2, red, cone inte
  - item: i3, red, cone inte
  - item: i0, green, cone inte
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - prédire oui si: theory2, couleur et bleu
  - theory1 théorie taille 2
  - prédire oui si: theory1, forme et cône
  - theory2 théorie taille 2
  - theory3 théorie taille 4
  - theory0 théorie taille 4
  - prédire oui si: theory0, couleur et bleu
  - prédire oui si: theory3, forme et cône
  - prédire oui si: theory0, couleur et vert
  - prédire oui si: theory3, couleur et bleu
Coûts : exception coûter 3.
Les données sont:
  - article: i7, bleu, cône et no
  - article: i1, rouge, cône et oui
  - article: i4, rouge, sphère et oui
  - article: i5, vert, cube et oui
  - article: i6, rouge, cône et no
  - article: i2, rouge, cône et no
  - article: i3, rouge, cône et no
  - article: i0, vert, cône et no
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - predict jes se: theory2, koloro kaj blua
  - theory1 teorio grandeco 2
  - predict jes se: theory1, formi kaj strobilo
  - theory2 teorio grandeco 2
  - theory3 teorio grandeco 4
  - theory0 teorio grandeco 4
  - predict jes se: theory0, koloro kaj blua
  - predict jes se: theory3, formi kaj strobilo
  - predict jes se: theory0, koloro kaj verda
  - predict jes se: theory3, koloro kaj blua
Kosto pozistino: escepto kosti 3.
Datumo:
  - ero: i7, blua, strobilo kaj no
  - ero: i1, ruĝa, strobilo kaj jes
  - ero: i4, ruĝa, sfero kaj jes
  - ero: i5, verda, kubo kaj jes
  - ero: i6, ruĝa, strobilo kaj no
  - ero: i2, ruĝa, strobilo kaj no
  - ero: i3, ruĝa, strobilo kaj no
  - ero: i0, verda, strobilo kaj no
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - megjósol igen ha: theory2, szín és kék
  - theory1 elmélet méret 2
  - megjósol igen ha: theory1, alak és toboz
  - theory2 elmélet méret 2
  - theory3 elmélet méret 4
  - theory0 elmélet méret 4
  - megjósol igen ha: theory0, szín és kék
  - megjósol igen ha: theory3, alak és toboz
  - megjósol igen ha: theory0, szín és zöld
  - megjósol igen ha: theory3, szín és kék
Költség manöken: kivétel kerül 3.
Adat:
  - cikk: i7, kék, toboz és no
  - cikk: i1, piros, toboz és igen
  - cikk: i4, piros, gömb és igen
  - cikk: i5, zöld, kocka és igen
  - cikk: i6, piros, toboz és no
  - cikk: i2, piros, toboz és no
  - cikk: i3, piros, toboz és no
  - cikk: i0, zöld, toboz és no
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - predict係要是:theory2, 色及藍色嘅
  - theory1theory size2
  - predict係要是:theory1, 形狀及cone
  - theory2theory size2
  - theory3theory size4
  - theory0theory size4
  - predict係要是:theory0, 色及藍色嘅
  - predict係要是:theory3, 形狀及cone
  - predict係要是:theory0, 色及綠色嘅
  - predict係要是:theory3, 色及藍色嘅
cost model:exception cost:3.
數據:
  - item:i7, 藍色嘅, cone及no
  - item:i1, 紅, cone及係
  - item:i4, 紅, sphere及係
  - item:i5, 綠色嘅, cube及係
  - item:i6, 紅, cone及no
  - item:i2, 紅, cone及no
  - item:i3, 紅, cone及no
  - item:i0, 綠色嘅, cone及no
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - predict yes if: theory2, color とぅ blue
  - theory1 theory size 2
  - predict yes if: theory1, shape とぅ cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, color とぅ blue
  - predict yes if: theory3, shape とぅ cone
  - predict yes if: theory0, color とぅ green
  - predict yes if: theory3, color とぅ blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone とぅ no
  - item: i1, red, cone とぅ yes
  - item: i4, red, sphere とぅ yes
  - item: i5, green, cube とぅ yes
  - item: i6, red, cone とぅ no
  - item: i2, red, cone とぅ no
  - item: i3, red, cone とぅ no
  - item: i0, green, cone とぅ no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - predict иә егер: theory2, түс және көк
  - theory1 теория мөлшер 2
  - predict иә егер: theory1, нысан және бүршік
  - theory2 теория мөлшер 2
  - theory3 теория мөлшер 4
  - theory0 теория мөлшер 4
  - predict иә егер: theory0, түс және көк
  - predict иә егер: theory3, нысан және бүршік
  - predict иә егер: theory0, түс және жасыл
  - predict иә егер: theory3, түс және көк
Тұру үлгі: ерекшелік тұру 3.
Деректер:
  - зат: i7, көк, бүршік және жоқ
  - зат: i1, қызыл, бүршік және иә
  - зат: i4, қызыл, сфера және иә
  - зат: i5, жасыл, куб және иә
  - зат: i6, қызыл, бүршік және жоқ
  - зат: i2, қызыл, бүршік және жоқ
  - зат: i3, қызыл, бүршік және жоқ
  - зат: i0, жасыл, бүршік және жоқ
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - predict כֵּן אִם: theory2, צֶבַע וְ־ כָּחוֹל
  - theory1 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory1, צוּרָה וְ־ אצטרובל
  - theory2 תּוֹרָה גֹּדֶל 2
  - theory3 תּוֹרָה גֹּדֶל 4
  - theory0 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory0, צֶבַע וְ־ כָּחוֹל
  - predict כֵּן אִם: theory3, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory0, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory3, צֶבַע וְ־ כָּחוֹל
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 3.
נְתוּנִים:
  - אבזר: i7, כָּחוֹל, אצטרובל וְ־ no
  - אבזר: i1, אָדוֹם, אצטרובל וְ־ כֵּן
  - אבזר: i4, אָדוֹם, כַּדּוּר וְ־ כֵּן
  - אבזר: i5, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i6, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i2, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i3, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i0, ירוק מקנאה, אצטרובל וְ־ no
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - predict იახ if: theory2, color დო blue
  - theory1 theory size 2
  - predict იახ if: theory1, shape დო cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict იახ if: theory0, color დო blue
  - predict იახ if: theory3, shape დო cone
  - predict იახ if: theory0, color დო green
  - predict იახ if: theory3, color დო blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone დო no
  - item: i1, red, cone დო იახ
  - item: i4, red, sphere დო იახ
  - item: i5, green, cube დო იახ
  - item: i6, red, cone დო no
  - item: i2, red, cone დო no
  - item: i3, red, cone დო no
  - item: i0, green, cone დო no
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - predict ya jika: theory2, warna dan biru
  - theory1 teori ukuran 2
  - predict ya jika: theory1, bentuk dan kerucut
  - theory2 teori ukuran 2
  - theory3 teori ukuran 4
  - theory0 teori ukuran 4
  - predict ya jika: theory0, warna dan biru
  - predict ya jika: theory3, bentuk dan kerucut
  - predict ya jika: theory0, warna dan hijau
  - predict ya jika: theory3, warna dan biru
Cost model: pengecualian menghabiskan biaya 3.
Data:
  - benda: i7, biru, kerucut dan no
  - benda: i1, merah, kerucut dan ya
  - benda: i4, merah, bola dan ya
  - benda: i5, hijau, kubus dan ya
  - benda: i6, merah, kerucut dan no
  - benda: i2, merah, kerucut dan no
  - benda: i3, merah, kerucut dan no
  - benda: i0, hijau, kerucut dan no
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងខៀវ
  - theory1ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងកោណ
  - theory2ទ្រឹស្ដីទំហំ2
  - theory3ទ្រឹស្ដីទំហំ4
  - theory0ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងខៀវ
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងកោណ
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងខៀវ
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:3.
ទិន្នន័យ:
  - item:i7, ខៀវ, កោណនឹងទេ
  - item:i1, ក្រហម, កោណនឹងច៎ា
  - item:i4, ក្រហម, គោលនឹងច៎ា
  - item:i5, បៃតង, គូបនឹងច៎ា
  - item:i6, ក្រហម, កោណនឹងទេ
  - item:i2, ក្រហម, កោណនឹងទេ
  - item:i3, ក្រហម, កោណនឹងទេ
  - item:i0, បៃតង, កោណនឹងទេ
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຟ້າ
  - theory1ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະcone
  - theory2ທິດສະດີຂະຫນາດ2
  - theory3ທິດສະດີຂະຫນາດ4
  - theory0ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຟ້າ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະcone
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະຟ້າ
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:3.
ຂໍ້ມູນ:
  - item:i7, ຟ້າ, coneແລະno
  - item:i1, ແດງ, coneແລະແມ່ນ
  - item:i4, ແດງ, ຄະດີແລະແມ່ນ
  - item:i5, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i6, ແດງ, coneແລະno
  - item:i2, ແດງ, coneແລະno
  - item:i3, ແດງ, coneແລະno
  - item:i0, ຂຽວ, coneແລະno
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - predict అవును అయితే: theory2, రంగు మరియు నీలము
  - theory1 2 theory size
  - predict అవును అయితే: theory1, ఆకారం మరియు cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict అవును అయితే: theory0, రంగు మరియు నీలము
  - predict అవును అయితే: theory3, ఆకారం మరియు cone
  - predict అవును అయితే: theory0, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory3, రంగు మరియు నీలము
cost model: exception cost 3.
data:
  - item: i7, నీలము, cone మరియు no
  - item: i1, ఎర్ర, cone మరియు అవును
  - item: i4, ఎర్ర, గోళము మరియు అవును
  - item: i5, పచ్చని, cube మరియు అవును
  - item: i6, ఎర్ర, cone మరియు no
  - item: i2, ఎర్ర, cone మరియు no
  - item: i3, ఎర్ర, cone మరియు no
  - item: i0, పచ్చని, cone మరియు no
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - predict hẹn if: theory2, color dúdú
  - theory1 theory size 2
  - predict hẹn if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict hẹn if: theory0, color dúdú
  - predict hẹn if: theory3, shape cone
  - predict hẹn if: theory0, color green
  - predict hẹn if: theory3, color dúdú
Cost model: exception cost 3.
Data:
  - item: i7, dúdú, cone rárá
  - item: i1, pupa, cone bẹ́ẹ̀ ni
  - item: i4, pupa, sphere bẹ́ẹ̀ ni
  - item: i5, green, cube bẹ́ẹ̀ ni
  - item: i6, pupa, cone rárá
  - item: i2, pupa, cone rárá
  - item: i3, pupa, cone rárá
  - item: i0, green, cone rárá
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict au if: theory2, color blue
  - theory1 2 theory size
  - predict au if: theory1, shape cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict au if: theory0, color blue
  - predict au if: theory3, shape cone
  - predict au if: theory0, color green
  - predict au if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, łichíí, cone au
  - item: i4, łichíí, sphere au
  - item: i5, green, cube au
  - item: i6, łichíí, cone no
  - item: i2, łichíí, cone no
  - item: i3, łichíí, cone no
  - item: i0, green, cone no
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - predict yes if: theory2, color blue
  - theory1 theory size 2
  - predict yes if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color green
  - predict yes if: theory3, color blue
cost model: exception cost 3.
data:
  - item: i7, blue, cone ᐋᒃᑲ
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, green, cube yes
  - item: i6, red, cone ᐋᒃᑲ
  - item: i2, red, cone ᐋᒃᑲ
  - item: i3, red, cone ᐋᒃᑲ
  - item: i0, green, cone ᐋᒃᑲ
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color blaw
  - theory1 theory size 2
  - predict yes if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, color blaw
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color grun
  - predict yes if: theory3, color blaw
Cost model: exception cost 3.
Data:
  - item: i7, blaw, cone no
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, grun, cube yes
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, grun, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, color blue
  - theory1 theory size 2
  - predict yes if: theory1, shape cone
  - theory2 theory size 2
  - theory3 theory size 4
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color green
  - predict yes if: theory3, color blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, green, cube yes
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, green, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory2, ранг blue
  - theory1 2 theory size
  - predict yes if: theory1, shape cone
  - theory2 2 theory size
  - theory3 4 theory size
  - theory0 4 theory size
  - predict yes if: theory0, ранг blue
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, ранг къацу
  - predict yes if: theory3, ранг blue
Cost model: exception cost 3.
Data:
  - item: i7, blue, cone no
  - item: i1, red, cone yes
  - item: i4, red, sphere yes
  - item: i5, къацу, cube yes
  - item: i6, red, cone no
  - item: i2, red, cone no
  - item: i3, red, cone no
  - item: i0, къацу, cone no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - theory3 theory size 2
  - predicts yes if: theory3, colour and green
  - theory1 theory size 2
  - predicts yes if: theory1, colour and blue
  - theory0 theory size 4
  - predicts yes if: theory0, colour and blue
  - predicts yes if: theory2, colour and green
  - predicts yes if: theory0, colour and blue
  - theory2 theory size 2
Costs: exception cost 2.
The data are:
  - item: i4, red, cube and yes
  - item: i2, blue, cube and no
  - item: i6, green, cube and no
  - item: i1, green, cube and no
  - item: i3, blue, sphere and yes
  - item: i5, red, sphere and yes
  - item: i7, green, cube and yes
  - item: i0, red, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - theory3 el tamaño de la teoría 2
  - predice sí si: theory3, es de color y verde
  - theory1 el tamaño de la teoría 2
  - predice sí si: theory1, es de color y azul
  - theory0 el tamaño de la teoría 4
  - predice sí si: theory0, es de color y azul
  - predice sí si: theory2, es de color y verde
  - predice sí si: theory0, es de color y azul
  - theory2 el tamaño de la teoría 2
Costes: exception el coste: 2.
Los datos son:
  - el elemento: i4, rojo, cubo y sí
  - el elemento: i2, azul, cubo y no
  - el elemento: i6, verde, cubo y no
  - el elemento: i1, verde, cubo y no
  - el elemento: i3, azul, esfera y sí
  - el elemento: i5, rojo, esfera y sí
  - el elemento: i7, verde, cubo y sí
  - el elemento: i0, rojo, cubo y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - theory3的理论规模是2
  - 预测是若：theory3、颜色和绿色的
  - theory1的理论规模是2
  - 预测是若：theory1、颜色和蓝色的
  - theory0的理论规模是4
  - 预测是若：theory0、颜色和蓝色的
  - 预测是若：theory2、颜色和绿色的
  - 预测是若：theory0、颜色和蓝色的
  - theory2的理论规模是2
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i4、红色的、立方体和是
  - 的物品是：i2、蓝色的、立方体和否
  - 的物品是：i6、绿色的、立方体和否
  - 的物品是：i1、绿色的、立方体和否
  - 的物品是：i3、蓝色的、球体和是
  - 的物品是：i5、红色的、球体和是
  - 的物品是：i7、绿色的、立方体和是
  - 的物品是：i0、红色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - theory3 2 kuram boyut
  - öngörür evet eğer: theory3, renk ve yeşil
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory1, renk ve mavi
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, renk ve mavi
  - öngörür evet eğer: theory2, renk ve yeşil
  - öngörür evet eğer: theory0, renk ve mavi
  - theory2 2 kuram boyut
Cost model: exception maliyet: 2.
Veriler:
  - item: i4, kırmızı, küp ve evet
  - item: i2, mavi, küp ve hayır
  - item: i6, yeşil, küp ve hayır
  - item: i1, yeşil, küp ve hayır
  - item: i3, mavi, küre ve evet
  - item: i5, kırmızı, küre ve evet
  - item: i7, yeşil, küp ve evet
  - item: i0, kırmızı, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - theory3 theory size 2
  - predict ndiyo kama: theory3, rangi na kijani
  - theory1 theory size 2
  - predict ndiyo kama: theory1, rangi na buluu
  - theory0 theory size 4
  - predict ndiyo kama: theory0, rangi na buluu
  - predict ndiyo kama: theory2, rangi na kijani
  - predict ndiyo kama: theory0, rangi na buluu
  - theory2 theory size 2
Cost model: exception gharimu: 2.
Data:
  - item: i4, ekundu, mchemraba na ndiyo
  - item: i2, buluu, mchemraba na hapana
  - item: i6, kijani, mchemraba na hapana
  - item: i1, kijani, mchemraba na hapana
  - item: i3, buluu, tufe na ndiyo
  - item: i5, ekundu, tufe na ndiyo
  - item: i7, kijani, mchemraba na ndiyo
  - item: i0, ekundu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - theory3 theory size 2
  - predicts yes if: theory3, colour and green
  - theory1 theory size 2
  - predicts yes if: theory1, colour and blue
  - theory0 theory size 4
  - predicts yes if: theory0, colour and blue
  - predicts yes if: theory2, colour and green
  - predicts yes if: theory0, colour and blue
  - theory2 theory size 2
Costs: exception cost 2.
The data are:
  - item: i4, red, cube and yes
  - item: i2, blue, cube and no
  - item: i6, green, cube and no
  - item: i1, green, cube and no
  - item: i3, blue, sphere and yes
  - item: i5, red, sphere and yes
  - item: i7, green, cube and yes
  - item: i0, red, cube and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(theory_size theory3 2) (predicts_yes_if theory3 color green) (theory_size theory1 2) (predicts_yes_if theory1 color blue) (theory_size theory0 4) (predicts_yes_if theory0 color blue) (predicts_yes_if theory2 color green) (predicts_yes_if theory0 color blue) (theory_size theory2 2)], cost_model: [(exception_cost 2)], data: [(item i4 red cube yes) (item i2 blue cube no) (item i6 green cube no) (item i1 green cube no) (item i3 blue sphere yes) (item i5 red sphere yes) (item i7 green cube yes) (item i0 red cube yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - theory3 Theorie Größe 2
  - vorhersagen ja wenn: theory3, Farbe und grün
  - theory1 Theorie Größe 2
  - vorhersagen ja wenn: theory1, Farbe und blau
  - theory0 Theorie Größe 4
  - vorhersagen ja wenn: theory0, Farbe und blau
  - vorhersagen ja wenn: theory2, Farbe und grün
  - vorhersagen ja wenn: theory0, Farbe und blau
  - theory2 Theorie Größe 2
Kosten: Ausnahme kosten 2.
Die Daten sind:
  - Ding: i4, rot, Kubus und ja
  - Ding: i2, blau, Kubus und nein
  - Ding: i6, grün, Kubus und nein
  - Ding: i1, grün, Kubus und nein
  - Ding: i3, blau, Kugel und ja
  - Ding: i5, rot, Kugel und ja
  - Ding: i7, grün, Kubus und ja
  - Ding: i0, rot, Kubus und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - theory3 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory3, бо̀ја и зѐлен
  - theory1 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory1, бо̀ја и пла̑в
  - theory0 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory0, бо̀ја и пла̑в
  - predvidjeti да ȁko: theory2, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory0, бо̀ја и пла̑в
  - theory2 тѐо̄рија величѝна 2
Trošak мо̀дел: изузе́так ко̏штати 2.
Податци:
  - item: i4, цр̀вен, коцка и да
  - item: i2, пла̑в, коцка и no
  - item: i6, зѐлен, коцка и no
  - item: i1, зѐлен, коцка и no
  - item: i3, пла̑в, сфе́ра и да
  - item: i5, цр̀вен, сфе́ра и да
  - item: i7, зѐлен, коцка и да
  - item: i0, цр̀вен, коцка и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - theory3 teoria koko 2
  - ennakoida kyllä jos: theory3, väri ja vihreä
  - theory1 teoria koko 2
  - ennakoida kyllä jos: theory1, väri ja sininen
  - theory0 teoria koko 4
  - ennakoida kyllä jos: theory0, väri ja sininen
  - ennakoida kyllä jos: theory2, väri ja vihreä
  - ennakoida kyllä jos: theory0, väri ja sininen
  - theory2 teoria koko 2
Kustannus malli: poikkeus maksaa 2.
Tieto:
  - esine: i4, punainen, kuutio ja kyllä
  - esine: i2, sininen, kuutio ja ei
  - esine: i6, vihreä, kuutio ja ei
  - esine: i1, vihreä, kuutio ja ei
  - esine: i3, sininen, pallo ja kyllä
  - esine: i5, punainen, pallo ja kyllä
  - esine: i7, vihreä, kuutio ja kyllä
  - esine: i0, punainen, kuutio ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - theory3的理论规模是2
  - 预测是若：theory3、颜色和绿色的
  - theory1的理论规模是2
  - 预测是若：theory1、颜色和蓝色的
  - theory0的理论规模是4
  - 预测是若：theory0、颜色和蓝色的
  - 预测是若：theory2、颜色和绿色的
  - 预测是若：theory0、颜色和蓝色的
  - theory2的理论规模是2
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i4、红色的、立方体和是
  - 的物品是：i2、蓝色的、立方体和否
  - 的物品是：i6、绿色的、立方体和否
  - 的物品是：i1、绿色的、立方体和否
  - 的物品是：i3、蓝色的、球体和是
  - 的物品是：i5、红色的、球体和是
  - 的物品是：i7、绿色的、立方体和是
  - 的物品是：i0、红色的、立方体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - theory32理論大きさ
  - 予言するはいもし...:theory3, 色と緑の
  - theory12理論大きさ
  - 予言するはいもし...:theory1, 色と青い
  - theory04理論大きさ
  - 予言するはいもし...:theory0, 色と青い
  - 予言するはいもし...:theory2, 色と緑の
  - 予言するはいもし...:theory0, 色と青い
  - theory22理論大きさ
費用モデル:例外費やす:2.
資料:
  - 項目:i4, 赤い, 立方体とはい
  - 項目:i2, 青い, 立方体といいえ
  - 項目:i6, 緑の, 立方体といいえ
  - 項目:i1, 緑の, 立方体といいえ
  - 項目:i3, 青い, 球とはい
  - 項目:i5, 赤い, 球とはい
  - 項目:i7, 緑の, 立方体とはい
  - 項目:i0, 赤い, 立方体とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - theory3 2 kuram boyut
  - öngörür evet eğer: theory3, renk ve yeşil
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory1, renk ve mavi
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, renk ve mavi
  - öngörür evet eğer: theory2, renk ve yeşil
  - öngörür evet eğer: theory0, renk ve mavi
  - theory2 2 kuram boyut
Cost model: exception maliyet: 2.
Veriler:
  - item: i4, kırmızı, küp ve evet
  - item: i2, mavi, küp ve hayır
  - item: i6, yeşil, küp ve hayır
  - item: i1, yeşil, küp ve hayır
  - item: i3, mavi, küre ve evet
  - item: i5, kırmızı, küre ve evet
  - item: i7, yeşil, küp ve evet
  - item: i0, kırmızı, küp ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - نَظَرِيَّة مَقَاس theory3 2
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory1 2
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَزْرَق
  - نَظَرِيَّة مَقَاس theory0 4
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَزْرَق
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَزْرَق
  - نَظَرِيَّة مَقَاس theory2 2
cost model: اِسْتِثْنَاء cost 2.
بَيَانَات:
  - بَنْد: i4، أَحْمَر، كَعْبَة وَـ نَعَم
  - بَنْد: i2، أَزْرَق، كَعْبَة وَـ no
  - بَنْد: i6، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i1، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i3، أَزْرَق، كُرَة وَـ نَعَم
  - بَنْد: i5، أَحْمَر، كُرَة وَـ نَعَم
  - بَنْد: i7، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i0، أَحْمَر، كَعْبَة وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - theory3 2 თეორია ზომა
  - predict დიახ თუ: theory3, ფერი და მწვანე
  - theory1 2 თეორია ზომა
  - predict დიახ თუ: theory1, ფერი და ლურჯი
  - theory0 4 თეორია ზომა
  - predict დიახ თუ: theory0, ფერი და ლურჯი
  - predict დიახ თუ: theory2, ფერი და მწვანე
  - predict დიახ თუ: theory0, ფერი და ლურჯი
  - theory2 2 თეორია ზომა
Ფასი მოდელი: გამონაკლისი ღირს 2.
Მონაცემები:
  - item: i4, წითელი, კუბი და დიახ
  - item: i2, ლურჯი, კუბი და no
  - item: i6, მწვანე, კუბი და no
  - item: i1, მწვანე, კუბი და no
  - item: i3, ლურჯი, სფერო და დიახ
  - item: i5, წითელი, სფერო და დიახ
  - item: i7, მწვანე, კუბი და დიახ
  - item: i0, წითელი, კუბი და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - ariā size theory3 2
  - matakite āe mēnā: theory3, kano me kākāriki
  - ariā size theory1 2
  - matakite āe mēnā: theory1, kano me kahurangi
  - ariā size theory0 4
  - matakite āe mēnā: theory0, kano me kahurangi
  - matakite āe mēnā: theory2, kano me kākāriki
  - matakite āe mēnā: theory0, kano me kahurangi
  - ariā size theory2 2
Cost model: hāungatanga cost 2.
Hōtuku:
  - tūemi: i4, red, mataono me āe
  - tūemi: i2, kahurangi, mataono me kāo
  - tūemi: i6, kākāriki, mataono me kāo
  - tūemi: i1, kākāriki, mataono me kāo
  - tūemi: i3, kahurangi, poi me āe
  - tūemi: i5, red, poi me āe
  - tūemi: i7, kākāriki, mataono me āe
  - tūemi: i0, red, mataono me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - theory3 lý thuyết kích thước 2
  - dự báo vâng nếu: theory3, màu và green
  - theory1 lý thuyết kích thước 2
  - dự báo vâng nếu: theory1, màu và blue
  - theory0 lý thuyết kích thước 4
  - dự báo vâng nếu: theory0, màu và blue
  - dự báo vâng nếu: theory2, màu và green
  - dự báo vâng nếu: theory0, màu và blue
  - theory2 lý thuyết kích thước 2
Cost model: lệ ngoại cost 2.
Dữ liệu:
  - vật phẩm: i4, đỏ, lập phương và vâng
  - vật phẩm: i2, blue, lập phương và no
  - vật phẩm: i6, green, lập phương và no
  - vật phẩm: i1, green, lập phương và no
  - vật phẩm: i3, blue, mặt cầu và vâng
  - vật phẩm: i5, đỏ, mặt cầu và vâng
  - vật phẩm: i7, green, lập phương và vâng
  - vật phẩm: i0, đỏ, lập phương và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - theory3ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory3, สีและเขียว
  - theory1ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory1, สีและน้ำเงิน
  - theory0ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory0, สีและน้ำเงิน
  - ทำนายใช่ถ้า หาก:theory2, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory0, สีและน้ำเงิน
  - theory2ทฤษฎีขนาด2
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:2.
ข้อมูล:
  - item:i4, แดง, ลูกบาศก์และใช่
  - item:i2, น้ำเงิน, ลูกบาศก์และไฮ้
  - item:i6, เขียว, ลูกบาศก์และไฮ้
  - item:i1, เขียว, ลูกบาศก์และไฮ้
  - item:i3, น้ำเงิน, ทรงกลมและใช่
  - item:i5, แดง, ทรงกลมและใช่
  - item:i7, เขียว, ลูกบาศก์และใช่
  - item:i0, แดง, ลูกบาศก์และใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - theory3 2 theory size
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் பச்சை
  - theory1 2 theory size
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் நீலம்
  - theory0 4 theory size
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் நீலம்
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் நீலம்
  - theory2 2 theory size
cost model: exception செலவு 2.
தரவுகள்:
  - item: i4, சிவப்பு, cube மற்றும் ஆம்
  - item: i2, நீலம், cube மற்றும் no
  - item: i6, பச்சை, cube மற்றும் no
  - item: i1, பச்சை, cube மற்றும் no
  - item: i3, நீலம், sphere மற்றும் ஆம்
  - item: i5, சிவப்பு, sphere மற்றும் ஆம்
  - item: i7, பச்சை, cube மற்றும் ஆம்
  - item: i0, சிவப்பு, cube மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - theory3 theory size 2
  - predict ndiyo kama: theory3, rangi na kijani
  - theory1 theory size 2
  - predict ndiyo kama: theory1, rangi na buluu
  - theory0 theory size 4
  - predict ndiyo kama: theory0, rangi na buluu
  - predict ndiyo kama: theory2, rangi na kijani
  - predict ndiyo kama: theory0, rangi na buluu
  - theory2 theory size 2
Cost model: exception gharimu: 2.
Data:
  - item: i4, ekundu, mchemraba na ndiyo
  - item: i2, buluu, mchemraba na hapana
  - item: i6, kijani, mchemraba na hapana
  - item: i1, kijani, mchemraba na hapana
  - item: i3, buluu, tufe na ndiyo
  - item: i5, ekundu, tufe na ndiyo
  - item: i7, kijani, mchemraba na ndiyo
  - item: i0, ekundu, mchemraba na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - theory3 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory1 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dootłʼizh
  - theory0 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dootłʼizh
  - theory2 2 theory size
Cost model: exception bą́ą́h ílį́ 2.
Data:
  - item: i4, łichííʼ, cube dóó aooʼ
  - item: i2, dootłʼizh, cube dóó no
  - item: i6, dinootłʼizh, cube dóó no
  - item: i1, dinootłʼizh, cube dóó no
  - item: i3, dootłʼizh, sphere dóó aooʼ
  - item: i5, łichííʼ, sphere dóó aooʼ
  - item: i7, dinootłʼizh, cube dóó aooʼ
  - item: i0, łichííʼ, cube dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - theory3 2 theory size
  - predict aap if: theory3, qalipaat aamma qorsuk
  - theory1 2 theory size
  - predict aap if: theory1, qalipaat aamma tungujortoq
  - theory0 4 theory size
  - predict aap if: theory0, qalipaat aamma tungujortoq
  - predict aap if: theory2, qalipaat aamma qorsuk
  - predict aap if: theory0, qalipaat aamma tungujortoq
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, aappalajaarpoq, cube aamma aap
  - item: i2, tungujortoq, cube aamma no
  - item: i6, qorsuk, cube aamma no
  - item: i1, qorsuk, cube aamma no
  - item: i3, tungujortoq, sphere aamma aap
  - item: i5, aappalajaarpoq, sphere aamma aap
  - item: i7, qorsuk, cube aamma aap
  - item: i0, aappalajaarpoq, cube aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict ya sapos: theory3, kala na grinpela
  - theory1 theory size 2
  - predict ya sapos: theory1, kala na blu
  - theory0 theory size 4
  - predict ya sapos: theory0, kala na blu
  - predict ya sapos: theory2, kala na grinpela
  - predict ya sapos: theory0, kala na blu
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, ret, cube na ya
  - item: i2, blu, cube na no
  - item: i6, grinpela, cube na no
  - item: i1, grinpela, cube na no
  - item: i3, blu, sphere na ya
  - item: i5, ret, sphere na ya
  - item: i7, grinpela, cube na ya
  - item: i0, ret, cube na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, color green
  - theory1 theory size 2
  - predict yes if: theory1, color blue
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory2, color green
  - predict yes if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, màxke, cube yes
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere yes
  - item: i5, màxke, sphere yes
  - item: i7, green, cube yes
  - item: i0, màxke, cube yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, бос а баьццара
  - theory1 theory size 2
  - predict yes if: theory1, бос а blue
  - theory0 theory size 4
  - predict yes if: theory0, бос а blue
  - predict yes if: theory2, бос а баьццара
  - predict yes if: theory0, бос а blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, цӏе, cube а yes
  - item: i2, blue, cube а no
  - item: i6, баьццара, cube а no
  - item: i1, баьццара, cube а no
  - item: i3, blue, sphere а yes
  - item: i5, цӏе, sphere а yes
  - item: i7, баьццара, cube а yes
  - item: i0, цӏе, cube а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - theory3 2 이론 크기
  - 예언하다 네 만약: theory3, 색깔 녹색의
  - theory1 2 이론 크기
  - 예언하다 네 만약: theory1, 색깔 푸르다
  - theory0 4 이론 크기
  - 예언하다 네 만약: theory0, 색깔 푸르다
  - 예언하다 네 만약: theory2, 색깔 녹색의
  - 예언하다 네 만약: theory0, 색깔 푸르다
  - theory2 2 이론 크기
cost model: 예외 cost 2.
자료:
  - 아이템: i4, 빨갛다, 정육면체 네
  - 아이템: i2, 푸르다, 정육면체 아니요
  - 아이템: i6, 녹색의, 정육면체 아니요
  - 아이템: i1, 녹색의, 정육면체 아니요
  - 아이템: i3, 푸르다, 구체 네
  - 아이템: i5, 빨갛다, 구체 네
  - 아이템: i7, 녹색의, 정육면체 네
  - 아이템: i0, 빨갛다, 정육면체 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - theory3 2 theory tamaina
  - aurresan bai if: theory3, kolore eta berde
  - theory1 2 theory tamaina
  - aurresan bai if: theory1, kolore eta urdin
  - theory0 4 theory tamaina
  - aurresan bai if: theory0, kolore eta urdin
  - aurresan bai if: theory2, kolore eta berde
  - aurresan bai if: theory0, kolore eta urdin
  - theory2 2 theory tamaina
Cost model: exception kostatu 2.
Data:
  - item: i4, gorri, kubo eta bai
  - item: i2, urdin, kubo eta no
  - item: i6, berde, kubo eta no
  - item: i1, berde, kubo eta no
  - item: i3, urdin, esfera eta bai
  - item: i5, gorri, esfera eta bai
  - item: i7, berde, kubo eta bai
  - item: i0, gorri, kubo eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - theory3 theory size 2
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory1 theory size 2
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - theory0 theory size 4
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, ᎩᎦᎨ, cube ᎠᎴ ᎥᎥ
  - item: i2, ᏌᎪᏂᎨᎢ, cube ᎠᎴ ᏝᏃ
  - item: i6, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i1, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i3, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᎥᎥ
  - item: i5, ᎩᎦᎨ, sphere ᎠᎴ ᎥᎥ
  - item: i7, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i0, ᎩᎦᎨ, cube ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory3 theory chhika 2
  - predict arí if: theory3, llimphi waylla
  - theory1 theory chhika 2
  - predict arí if: theory1, llimphi anqas
  - theory0 theory chhika 4
  - predict arí if: theory0, llimphi anqas
  - predict arí if: theory2, llimphi waylla
  - predict arí if: theory0, llimphi anqas
  - theory2 theory chhika 2
Cost model: exception cost 2.
Data:
  - item: i4, puka, cube arí
  - item: i2, anqas, cube mánan
  - item: i6, waylla, cube mánan
  - item: i1, waylla, cube mánan
  - item: i3, anqas, sphere arí
  - item: i5, puka, sphere arí
  - item: i7, waylla, cube arí
  - item: i0, puka, cube arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 2 тиориэ size
  - predict yes if: theory3, color ыкӏи уцышъо
  - theory1 2 тиориэ size
  - predict yes if: theory1, color ыкӏи blue
  - theory0 4 тиориэ size
  - predict yes if: theory0, color ыкӏи blue
  - predict yes if: theory2, color ыкӏи уцышъо
  - predict yes if: theory0, color ыкӏи blue
  - theory2 2 тиориэ size
Cost model: exception cost 2.
Data:
  - item: i4, плъыжьы, cube ыкӏи yes
  - item: i2, blue, cube ыкӏи no
  - item: i6, уцышъо, cube ыкӏи no
  - item: i1, уцышъо, cube ыкӏи no
  - item: i3, blue, sphere ыкӏи yes
  - item: i5, плъыжьы, sphere ыкӏи yes
  - item: i7, уцышъо, cube ыкӏи yes
  - item: i0, плъыжьы, cube ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, xim thiab ntsuab
  - theory1 theory size 2
  - predict yes if: theory1, xim thiab xiav
  - theory0 theory size 4
  - predict yes if: theory0, xim thiab xiav
  - predict yes if: theory2, xim thiab ntsuab
  - predict yes if: theory0, xim thiab xiav
  - theory2 theory size 2
Cost model: exception nqi 2.
Data:
  - item: i4, liab, cube thiab yes
  - item: i2, xiav, cube thiab no
  - item: i6, ntsuab, cube thiab no
  - item: i1, ntsuab, cube thiab no
  - item: i3, xiav, sphere thiab yes
  - item: i5, liab, sphere thiab yes
  - item: i7, ntsuab, cube thiab yes
  - item: i0, liab, cube thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict ngai if: theory3, color green
  - theory1 theory size 2
  - predict ngai if: theory1, color blue
  - theory0 theory size 4
  - predict ngai if: theory0, color blue
  - predict ngai if: theory2, color green
  - predict ngai if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube ngai
  - item: i2, blue, cube woi
  - item: i6, green, cube woi
  - item: i1, green, cube woi
  - item: i3, blue, sphere ngai
  - item: i5, red, sphere ngai
  - item: i7, green, cube ngai
  - item: i0, red, cube ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - theory3 theory size 2
  - predict i̠i if: theory3, color micha' green
  - theory1 theory size 2
  - predict i̠i if: theory1, color micha' blue
  - theory0 theory size 4
  - predict i̠i if: theory0, color micha' blue
  - predict i̠i if: theory2, color micha' green
  - predict i̠i if: theory0, color micha' blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, homa, cube micha' i̠i
  - item: i2, blue, cube micha' no
  - item: i6, green, cube micha' no
  - item: i1, green, cube micha' no
  - item: i3, blue, sphere micha' i̠i
  - item: i5, homa, sphere micha' i̠i
  - item: i7, green, cube micha' i̠i
  - item: i0, homa, cube micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - theory3 2 theory size
  - predict yes if: theory3, color カ green
  - theory1 2 theory size
  - predict yes if: theory1, color カ blue
  - theory0 4 theory size
  - predict yes if: theory0, color カ blue
  - predict yes if: theory2, color カ green
  - predict yes if: theory0, color カ blue
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, フレ, cube カ yes
  - item: i2, blue, cube カ ソモ
  - item: i6, green, cube カ ソモ
  - item: i1, green, cube カ ソモ
  - item: i3, blue, sphere カ yes
  - item: i5, フレ, sphere カ yes
  - item: i7, green, cube カ yes
  - item: i0, フレ, cube カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict yes эвыр: theory3, пэран ынкъам green
  - theory1 theory size 2
  - predict yes эвыр: theory1, пэран ынкъам ныԓиԓиԓӄин
  - theory0 theory size 4
  - predict yes эвыр: theory0, пэран ынкъам ныԓиԓиԓӄин
  - predict yes эвыр: theory2, пэран ынкъам green
  - predict yes эвыр: theory0, пэран ынкъам ныԓиԓиԓӄин
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, чеԓгыԓьын, cube ынкъам yes
  - item: i2, ныԓиԓиԓӄин, cube ынкъам no
  - item: i6, green, cube ынкъам no
  - item: i1, green, cube ынкъам no
  - item: i3, ныԓиԓиԓӄин, sphere ынкъам yes
  - item: i5, чеԓгыԓьын, sphere ынкъам yes
  - item: i7, green, cube ынкъам yes
  - item: i0, чеԓгыԓьын, cube ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict yes wa: theory3, color yéetel yaʼax
  - theory1 theory size 2
  - predict yes wa: theory1, color yéetel blue
  - theory0 theory size 4
  - predict yes wa: theory0, color yéetel blue
  - predict yes wa: theory2, color yéetel yaʼax
  - predict yes wa: theory0, color yéetel blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, chak, cube yéetel yes
  - item: i2, blue, cube yéetel no
  - item: i6, yaʼax, cube yéetel no
  - item: i1, yaʼax, cube yéetel no
  - item: i3, blue, sphere yéetel yes
  - item: i5, chak, sphere yéetel yes
  - item: i7, yaʼax, cube yéetel yes
  - item: i0, chak, cube yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict hą if: theory3, color green
  - theory1 theory size 2
  - predict hą if: theory1, color blue
  - theory0 theory size 4
  - predict hą if: theory0, color blue
  - predict hą if: theory2, color green
  - predict hą if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube hą
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere hą
  - item: i5, red, sphere hą
  - item: i7, green, cube hą
  - item: i0, red, cube hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - theory3 2 theory size
  - predict yes if: theory3, color na green
  - theory1 2 theory size
  - predict yes if: theory1, color na blue
  - theory0 4 theory size
  - predict yes if: theory0, color na blue
  - predict yes if: theory2, color na green
  - predict yes if: theory0, color na blue
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, red, cube na yes
  - item: i2, blue, cube na no
  - item: i6, green, cube na no
  - item: i1, green, cube na no
  - item: i3, blue, sphere na yes
  - item: i5, red, sphere na yes
  - item: i7, green, cube na yes
  - item: i0, red, cube na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict heẽ if: theory3, color green
  - theory1 theory size 2
  - predict heẽ if: theory1, color blue
  - theory0 theory size 4
  - predict heẽ if: theory0, color blue
  - predict heẽ if: theory2, color green
  - predict heẽ if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, pytã, cube heẽ
  - item: i2, blue, cube nahániri
  - item: i6, green, cube nahániri
  - item: i1, green, cube nahániri
  - item: i3, blue, sphere heẽ
  - item: i5, pytã, sphere heẽ
  - item: i7, green, cube heẽ
  - item: i0, pytã, cube heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 2 theory size
  - predict jisa if: theory3, sami ch’uxña
  - theory1 2 theory size
  - predict jisa if: theory1, sami larama
  - theory0 4 theory size
  - predict jisa if: theory0, sami larama
  - predict jisa if: theory2, sami ch’uxña
  - predict jisa if: theory0, sami larama
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, chupika, cube jisa
  - item: i2, larama, cube no
  - item: i6, ch’uxña, cube no
  - item: i1, ch’uxña, cube no
  - item: i3, larama, sphere jisa
  - item: i5, chupika, sphere jisa
  - item: i7, ch’uxña, cube jisa
  - item: i0, chupika, cube jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory ʻǁnàhã 2
  - predict ʻée if: theory3, color ǂʻá green
  - theory1 theory ʻǁnàhã 2
  - predict ʻée if: theory1, color ǂʻá blue
  - theory0 theory ʻǁnàhã 4
  - predict ʻée if: theory0, color ǂʻá blue
  - predict ʻée if: theory2, color ǂʻá green
  - predict ʻée if: theory0, color ǂʻá blue
  - theory2 theory ʻǁnàhã 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube ǂʻá ʻée
  - item: i2, blue, cube ǂʻá ʻĩĩ
  - item: i6, green, cube ǂʻá ʻĩĩ
  - item: i1, green, cube ǂʻá ʻĩĩ
  - item: i3, blue, sphere ǂʻá ʻée
  - item: i5, red, sphere ǂʻá ʻée
  - item: i7, green, cube ǂʻá ʻée
  - item: i0, red, cube ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 2 theory size
  - predict yes if: theory3, вал green
  - theory1 2 theory size
  - predict yes if: theory1, вал blue
  - theory0 4 theory size
  - predict yes if: theory0, вал blue
  - predict yes if: theory2, вал green
  - predict yes if: theory0, вал blue
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, red, cube yes
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere yes
  - item: i5, red, sphere yes
  - item: i7, green, cube yes
  - item: i0, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, color green
  - theory1 theory size 2
  - predict yes if: theory1, color blue
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory2, color green
  - predict yes if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube yes
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere yes
  - item: i5, red, sphere yes
  - item: i7, green, cube yes
  - item: i0, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 2 theory size
  - predict yes if: theory3, color green
  - theory1 2 theory size
  - predict yes if: theory1, color blue
  - theory0 4 theory size
  - predict yes if: theory0, color blue
  - predict yes if: theory2, color green
  - predict yes if: theory0, color blue
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, red, cube yes
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere yes
  - item: i5, red, sphere yes
  - item: i7, green, cube yes
  - item: i0, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict ee if: theory3, color green
  - theory1 theory size 2
  - predict ee if: theory1, color blue
  - theory0 theory size 4
  - predict ee if: theory0, color blue
  - predict ee if: theory2, color green
  - predict ee if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube ee
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere ee
  - item: i5, red, sphere ee
  - item: i7, green, cube ee
  - item: i0, red, cube ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, color green
  - theory1 theory size 2
  - predict yes if: theory1, color blue
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory2, color green
  - predict yes if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube yes
  - item: i2, blue, cube inte
  - item: i6, green, cube inte
  - item: i1, green, cube inte
  - item: i3, blue, sphere yes
  - item: i5, red, sphere yes
  - item: i7, green, cube yes
  - item: i0, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - theory3 théorie taille 2
  - prédire oui si: theory3, couleur et vert
  - theory1 théorie taille 2
  - prédire oui si: theory1, couleur et bleu
  - theory0 théorie taille 4
  - prédire oui si: theory0, couleur et bleu
  - prédire oui si: theory2, couleur et vert
  - prédire oui si: theory0, couleur et bleu
  - theory2 théorie taille 2
Coûts : exception coûter 2.
Les données sont:
  - article: i4, rouge, cube et oui
  - article: i2, bleu, cube et no
  - article: i6, vert, cube et no
  - article: i1, vert, cube et no
  - article: i3, bleu, sphère et oui
  - article: i5, rouge, sphère et oui
  - article: i7, vert, cube et oui
  - article: i0, rouge, cube et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - theory3 teorio grandeco 2
  - predict jes se: theory3, koloro kaj verda
  - theory1 teorio grandeco 2
  - predict jes se: theory1, koloro kaj blua
  - theory0 teorio grandeco 4
  - predict jes se: theory0, koloro kaj blua
  - predict jes se: theory2, koloro kaj verda
  - predict jes se: theory0, koloro kaj blua
  - theory2 teorio grandeco 2
Kosto pozistino: escepto kosti 2.
Datumo:
  - ero: i4, ruĝa, kubo kaj jes
  - ero: i2, blua, kubo kaj no
  - ero: i6, verda, kubo kaj no
  - ero: i1, verda, kubo kaj no
  - ero: i3, blua, sfero kaj jes
  - ero: i5, ruĝa, sfero kaj jes
  - ero: i7, verda, kubo kaj jes
  - ero: i0, ruĝa, kubo kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - theory3 elmélet méret 2
  - megjósol igen ha: theory3, szín és zöld
  - theory1 elmélet méret 2
  - megjósol igen ha: theory1, szín és kék
  - theory0 elmélet méret 4
  - megjósol igen ha: theory0, szín és kék
  - megjósol igen ha: theory2, szín és zöld
  - megjósol igen ha: theory0, szín és kék
  - theory2 elmélet méret 2
Költség manöken: kivétel kerül 2.
Adat:
  - cikk: i4, piros, kocka és igen
  - cikk: i2, kék, kocka és no
  - cikk: i6, zöld, kocka és no
  - cikk: i1, zöld, kocka és no
  - cikk: i3, kék, gömb és igen
  - cikk: i5, piros, gömb és igen
  - cikk: i7, zöld, kocka és igen
  - cikk: i0, piros, kocka és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - theory3theory size2
  - predict係要是:theory3, 色及綠色嘅
  - theory1theory size2
  - predict係要是:theory1, 色及藍色嘅
  - theory0theory size4
  - predict係要是:theory0, 色及藍色嘅
  - predict係要是:theory2, 色及綠色嘅
  - predict係要是:theory0, 色及藍色嘅
  - theory2theory size2
cost model:exception cost:2.
數據:
  - item:i4, 紅, cube及係
  - item:i2, 藍色嘅, cube及no
  - item:i6, 綠色嘅, cube及no
  - item:i1, 綠色嘅, cube及no
  - item:i3, 藍色嘅, sphere及係
  - item:i5, 紅, sphere及係
  - item:i7, 綠色嘅, cube及係
  - item:i0, 紅, cube及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, color とぅ green
  - theory1 theory size 2
  - predict yes if: theory1, color とぅ blue
  - theory0 theory size 4
  - predict yes if: theory0, color とぅ blue
  - predict yes if: theory2, color とぅ green
  - predict yes if: theory0, color とぅ blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube とぅ yes
  - item: i2, blue, cube とぅ no
  - item: i6, green, cube とぅ no
  - item: i1, green, cube とぅ no
  - item: i3, blue, sphere とぅ yes
  - item: i5, red, sphere とぅ yes
  - item: i7, green, cube とぅ yes
  - item: i0, red, cube とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - theory3 теория мөлшер 2
  - predict иә егер: theory3, түс және жасыл
  - theory1 теория мөлшер 2
  - predict иә егер: theory1, түс және көк
  - theory0 теория мөлшер 4
  - predict иә егер: theory0, түс және көк
  - predict иә егер: theory2, түс және жасыл
  - predict иә егер: theory0, түс және көк
  - theory2 теория мөлшер 2
Тұру үлгі: ерекшелік тұру 2.
Деректер:
  - зат: i4, қызыл, куб және иә
  - зат: i2, көк, куб және жоқ
  - зат: i6, жасыл, куб және жоқ
  - зат: i1, жасыл, куб және жоқ
  - зат: i3, көк, сфера және иә
  - зат: i5, қызыл, сфера және иә
  - зат: i7, жасыл, куб және иә
  - зат: i0, қызыл, куб және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - theory3 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory3, צֶבַע וְ־ ירוק מקנאה
  - theory1 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory1, צֶבַע וְ־ כָּחוֹל
  - theory0 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory0, צֶבַע וְ־ כָּחוֹל
  - predict כֵּן אִם: theory2, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory0, צֶבַע וְ־ כָּחוֹל
  - theory2 תּוֹרָה גֹּדֶל 2
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 2.
נְתוּנִים:
  - אבזר: i4, אָדוֹם, קובייה וְ־ כֵּן
  - אבזר: i2, כָּחוֹל, קובייה וְ־ no
  - אבזר: i6, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i1, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i3, כָּחוֹל, כַּדּוּר וְ־ כֵּן
  - אבזר: i5, אָדוֹם, כַּדּוּר וְ־ כֵּן
  - אבזר: i7, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i0, אָדוֹם, קובייה וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - theory3 theory size 2
  - predict იახ if: theory3, color დო green
  - theory1 theory size 2
  - predict იახ if: theory1, color დო blue
  - theory0 theory size 4
  - predict იახ if: theory0, color დო blue
  - predict იახ if: theory2, color დო green
  - predict იახ if: theory0, color დო blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube დო იახ
  - item: i2, blue, cube დო no
  - item: i6, green, cube დო no
  - item: i1, green, cube დო no
  - item: i3, blue, sphere დო იახ
  - item: i5, red, sphere დო იახ
  - item: i7, green, cube დო იახ
  - item: i0, red, cube დო იახ
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - theory3 teori ukuran 2
  - predict ya jika: theory3, warna dan hijau
  - theory1 teori ukuran 2
  - predict ya jika: theory1, warna dan biru
  - theory0 teori ukuran 4
  - predict ya jika: theory0, warna dan biru
  - predict ya jika: theory2, warna dan hijau
  - predict ya jika: theory0, warna dan biru
  - theory2 teori ukuran 2
Cost model: pengecualian menghabiskan biaya 2.
Data:
  - benda: i4, merah, kubus dan ya
  - benda: i2, biru, kubus dan no
  - benda: i6, hijau, kubus dan no
  - benda: i1, hijau, kubus dan no
  - benda: i3, biru, bola dan ya
  - benda: i5, merah, bola dan ya
  - benda: i7, hijau, kubus dan ya
  - benda: i0, merah, kubus dan ya
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - theory3ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងបៃតង
  - theory1ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងខៀវ
  - theory0ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងខៀវ
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងខៀវ
  - theory2ទ្រឹស្ដីទំហំ2
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:2.
ទិន្នន័យ:
  - item:i4, ក្រហម, គូបនឹងច៎ា
  - item:i2, ខៀវ, គូបនឹងទេ
  - item:i6, បៃតង, គូបនឹងទេ
  - item:i1, បៃតង, គូបនឹងទេ
  - item:i3, ខៀវ, គោលនឹងច៎ា
  - item:i5, ក្រហម, គោលនឹងច៎ា
  - item:i7, បៃតង, គូបនឹងច៎ា
  - item:i0, ក្រហម, គូបនឹងច៎ា
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - theory3ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະຂຽວ
  - theory1ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຟ້າ
  - theory0ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຟ້າ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຟ້າ
  - theory2ທິດສະດີຂະຫນາດ2
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:2.
ຂໍ້ມູນ:
  - item:i4, ແດງ, ລູກບາດແລະແມ່ນ
  - item:i2, ຟ້າ, ລູກບາດແລະno
  - item:i6, ຂຽວ, ລູກບາດແລະno
  - item:i1, ຂຽວ, ລູກບາດແລະno
  - item:i3, ຟ້າ, ຄະດີແລະແມ່ນ
  - item:i5, ແດງ, ຄະດີແລະແມ່ນ
  - item:i7, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i0, ແດງ, ລູກບາດແລະແມ່ນ
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - theory3 2 theory size
  - predict అవును అయితే: theory3, రంగు మరియు పచ్చని
  - theory1 2 theory size
  - predict అవును అయితే: theory1, రంగు మరియు నీలము
  - theory0 4 theory size
  - predict అవును అయితే: theory0, రంగు మరియు నీలము
  - predict అవును అయితే: theory2, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory0, రంగు మరియు నీలము
  - theory2 2 theory size
cost model: exception cost 2.
data:
  - item: i4, ఎర్ర, cube మరియు అవును
  - item: i2, నీలము, cube మరియు no
  - item: i6, పచ్చని, cube మరియు no
  - item: i1, పచ్చని, cube మరియు no
  - item: i3, నీలము, గోళము మరియు అవును
  - item: i5, ఎర్ర, గోళము మరియు అవును
  - item: i7, పచ్చని, cube మరియు అవును
  - item: i0, ఎర్ర, cube మరియు అవును
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - theory3 theory size 2
  - predict hẹn if: theory3, color green
  - theory1 theory size 2
  - predict hẹn if: theory1, color dúdú
  - theory0 theory size 4
  - predict hẹn if: theory0, color dúdú
  - predict hẹn if: theory2, color green
  - predict hẹn if: theory0, color dúdú
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, pupa, cube bẹ́ẹ̀ ni
  - item: i2, dúdú, cube rárá
  - item: i6, green, cube rárá
  - item: i1, green, cube rárá
  - item: i3, dúdú, sphere bẹ́ẹ̀ ni
  - item: i5, pupa, sphere bẹ́ẹ̀ ni
  - item: i7, green, cube bẹ́ẹ̀ ni
  - item: i0, pupa, cube bẹ́ẹ̀ ni
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory3 2 theory size
  - predict au if: theory3, color green
  - theory1 2 theory size
  - predict au if: theory1, color blue
  - theory0 4 theory size
  - predict au if: theory0, color blue
  - predict au if: theory2, color green
  - predict au if: theory0, color blue
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, łichíí, cube au
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere au
  - item: i5, łichíí, sphere au
  - item: i7, green, cube au
  - item: i0, łichíí, cube au
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - theory3 theory size 2
  - predict yes if: theory3, color green
  - theory1 theory size 2
  - predict yes if: theory1, color blue
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory2, color green
  - predict yes if: theory0, color blue
  - theory2 theory size 2
cost model: exception cost 2.
data:
  - item: i4, red, cube yes
  - item: i2, blue, cube ᐋᒃᑲ
  - item: i6, green, cube ᐋᒃᑲ
  - item: i1, green, cube ᐋᒃᑲ
  - item: i3, blue, sphere yes
  - item: i5, red, sphere yes
  - item: i7, green, cube yes
  - item: i0, red, cube yes
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, color grun
  - theory1 theory size 2
  - predict yes if: theory1, color blaw
  - theory0 theory size 4
  - predict yes if: theory0, color blaw
  - predict yes if: theory2, color grun
  - predict yes if: theory0, color blaw
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube yes
  - item: i2, blaw, cube no
  - item: i6, grun, cube no
  - item: i1, grun, cube no
  - item: i3, blaw, sphere yes
  - item: i5, red, sphere yes
  - item: i7, grun, cube yes
  - item: i0, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 theory size 2
  - predict yes if: theory3, color green
  - theory1 theory size 2
  - predict yes if: theory1, color blue
  - theory0 theory size 4
  - predict yes if: theory0, color blue
  - predict yes if: theory2, color green
  - predict yes if: theory0, color blue
  - theory2 theory size 2
Cost model: exception cost 2.
Data:
  - item: i4, red, cube yes
  - item: i2, blue, cube no
  - item: i6, green, cube no
  - item: i1, green, cube no
  - item: i3, blue, sphere yes
  - item: i5, red, sphere yes
  - item: i7, green, cube yes
  - item: i0, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory3 2 theory size
  - predict yes if: theory3, ранг къацу
  - theory1 2 theory size
  - predict yes if: theory1, ранг blue
  - theory0 4 theory size
  - predict yes if: theory0, ранг blue
  - predict yes if: theory2, ранг къацу
  - predict yes if: theory0, ранг blue
  - theory2 2 theory size
Cost model: exception cost 2.
Data:
  - item: i4, red, cube yes
  - item: i2, blue, cube no
  - item: i6, къацу, cube no
  - item: i1, къацу, cube no
  - item: i3, blue, sphere yes
  - item: i5, red, sphere yes
  - item: i7, къацу, cube yes
  - item: i0, red, cube yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - predicts yes if: theory3, shape and cube
  - predicts yes if: theory1, colour and blue
  - predicts yes if: theory1, colour and blue
  - theory2 theory size 2
  - predicts yes if: theory3, shape and cone
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory0, colour and green
  - theory3 theory size 4
  - predicts yes if: theory2, colour and blue
  - theory0 theory size 4
  - theory1 theory size 4
Costs: exception cost 4.
The data are:
  - item: i1, blue, sphere and no
  - item: i4, blue, sphere and yes
  - item: i5, green, cube and yes
  - item: i2, green, cube and no
  - item: i0, green, cube and yes
  - item: i6, red, cone and yes
  - item: i3, red, sphere and yes
  - item: i7, blue, sphere and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - predice sí si: theory3, forma y cubo
  - predice sí si: theory1, es de color y azul
  - predice sí si: theory1, es de color y azul
  - theory2 el tamaño de la teoría 2
  - predice sí si: theory3, forma y cono
  - predice sí si: theory0, es de color y rojo
  - predice sí si: theory0, es de color y verde
  - theory3 el tamaño de la teoría 4
  - predice sí si: theory2, es de color y azul
  - theory0 el tamaño de la teoría 4
  - theory1 el tamaño de la teoría 4
Costes: exception el coste: 4.
Los datos son:
  - el elemento: i1, azul, esfera y no
  - el elemento: i4, azul, esfera y sí
  - el elemento: i5, verde, cubo y sí
  - el elemento: i2, verde, cubo y no
  - el elemento: i0, verde, cubo y sí
  - el elemento: i6, rojo, cono y sí
  - el elemento: i3, rojo, esfera y sí
  - el elemento: i7, azul, esfera y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - 预测是若：theory3、形状和立方体
  - 预测是若：theory1、颜色和蓝色的
  - 预测是若：theory1、颜色和蓝色的
  - theory2的理论规模是2
  - 预测是若：theory3、形状和圆锥
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory0、颜色和绿色的
  - theory3的理论规模是4
  - 预测是若：theory2、颜色和蓝色的
  - theory0的理论规模是4
  - theory1的理论规模是4
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i1、蓝色的、球体和否
  - 的物品是：i4、蓝色的、球体和是
  - 的物品是：i5、绿色的、立方体和是
  - 的物品是：i2、绿色的、立方体和否
  - 的物品是：i0、绿色的、立方体和是
  - 的物品是：i6、红色的、圆锥和是
  - 的物品是：i3、红色的、球体和是
  - 的物品是：i7、蓝色的、球体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - öngörür evet eğer: theory3, şekil ve küp
  - öngörür evet eğer: theory1, renk ve mavi
  - öngörür evet eğer: theory1, renk ve mavi
  - theory2 2 kuram boyut
  - öngörür evet eğer: theory3, şekil ve koni
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory0, renk ve yeşil
  - theory3 4 kuram boyut
  - öngörür evet eğer: theory2, renk ve mavi
  - theory0 4 kuram boyut
  - theory1 4 kuram boyut
Cost model: exception maliyet: 4.
Veriler:
  - item: i1, mavi, küre ve hayır
  - item: i4, mavi, küre ve evet
  - item: i5, yeşil, küp ve evet
  - item: i2, yeşil, küp ve hayır
  - item: i0, yeşil, küp ve evet
  - item: i6, kırmızı, koni ve evet
  - item: i3, kırmızı, küre ve evet
  - item: i7, mavi, küre ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - predict ndiyo kama: theory3, umbo na mchemraba
  - predict ndiyo kama: theory1, rangi na buluu
  - predict ndiyo kama: theory1, rangi na buluu
  - theory2 theory size 2
  - predict ndiyo kama: theory3, umbo na koni
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory0, rangi na kijani
  - theory3 theory size 4
  - predict ndiyo kama: theory2, rangi na buluu
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception gharimu: 4.
Data:
  - item: i1, buluu, tufe na hapana
  - item: i4, buluu, tufe na ndiyo
  - item: i5, kijani, mchemraba na ndiyo
  - item: i2, kijani, mchemraba na hapana
  - item: i0, kijani, mchemraba na ndiyo
  - item: i6, ekundu, koni na ndiyo
  - item: i3, ekundu, tufe na ndiyo
  - item: i7, buluu, tufe na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - predicts yes if: theory3, shape and cube
  - predicts yes if: theory1, colour and blue
  - predicts yes if: theory1, colour and blue
  - theory2 theory size 2
  - predicts yes if: theory3, shape and cone
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory0, colour and green
  - theory3 theory size 4
  - predicts yes if: theory2, colour and blue
  - theory0 theory size 4
  - theory1 theory size 4
Costs: exception cost 4.
The data are:
  - item: i1, blue, sphere and no
  - item: i4, blue, sphere and yes
  - item: i5, green, cube and yes
  - item: i2, green, cube and no
  - item: i0, green, cube and yes
  - item: i6, red, cone and yes
  - item: i3, red, sphere and yes
  - item: i7, blue, sphere and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(predicts_yes_if theory3 shape cube) (predicts_yes_if theory1 color blue) (predicts_yes_if theory1 color blue) (theory_size theory2 2) (predicts_yes_if theory3 shape cone) (predicts_yes_if theory0 color red) (predicts_yes_if theory0 color green) (theory_size theory3 4) (predicts_yes_if theory2 color blue) (theory_size theory0 4) (theory_size theory1 4)], cost_model: [(exception_cost 4)], data: [(item i1 blue sphere no) (item i4 blue sphere yes) (item i5 green cube yes) (item i2 green cube no) (item i0 green cube yes) (item i6 red cone yes) (item i3 red sphere yes) (item i7 blue sphere yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - vorhersagen ja wenn: theory3, Form und Kubus
  - vorhersagen ja wenn: theory1, Farbe und blau
  - vorhersagen ja wenn: theory1, Farbe und blau
  - theory2 Theorie Größe 2
  - vorhersagen ja wenn: theory3, Form und Kegel
  - vorhersagen ja wenn: theory0, Farbe und rot
  - vorhersagen ja wenn: theory0, Farbe und grün
  - theory3 Theorie Größe 4
  - vorhersagen ja wenn: theory2, Farbe und blau
  - theory0 Theorie Größe 4
  - theory1 Theorie Größe 4
Kosten: Ausnahme kosten 4.
Die Daten sind:
  - Ding: i1, blau, Kugel und nein
  - Ding: i4, blau, Kugel und ja
  - Ding: i5, grün, Kubus und ja
  - Ding: i2, grün, Kubus und nein
  - Ding: i0, grün, Kubus und ja
  - Ding: i6, rot, Kegel und ja
  - Ding: i3, rot, Kugel und ja
  - Ding: i7, blau, Kugel und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - predvidjeti да ȁko: theory3, о̏блӣк и коцка
  - predvidjeti да ȁko: theory1, бо̀ја и пла̑в
  - predvidjeti да ȁko: theory1, бо̀ја и пла̑в
  - theory2 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory3, о̏блӣк и stožac
  - predvidjeti да ȁko: theory0, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory0, бо̀ја и зѐлен
  - theory3 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory2, бо̀ја и пла̑в
  - theory0 тѐо̄рија величѝна 4
  - theory1 тѐо̄рија величѝна 4
Trošak мо̀дел: изузе́так ко̏штати 4.
Податци:
  - item: i1, пла̑в, сфе́ра и no
  - item: i4, пла̑в, сфе́ра и да
  - item: i5, зѐлен, коцка и да
  - item: i2, зѐлен, коцка и no
  - item: i0, зѐлен, коцка и да
  - item: i6, цр̀вен, stožac и да
  - item: i3, цр̀вен, сфе́ра и да
  - item: i7, пла̑в, сфе́ра и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - ennakoida kyllä jos: theory3, muoto ja kuutio
  - ennakoida kyllä jos: theory1, väri ja sininen
  - ennakoida kyllä jos: theory1, väri ja sininen
  - theory2 teoria koko 2
  - ennakoida kyllä jos: theory3, muoto ja kartio
  - ennakoida kyllä jos: theory0, väri ja punainen
  - ennakoida kyllä jos: theory0, väri ja vihreä
  - theory3 teoria koko 4
  - ennakoida kyllä jos: theory2, väri ja sininen
  - theory0 teoria koko 4
  - theory1 teoria koko 4
Kustannus malli: poikkeus maksaa 4.
Tieto:
  - esine: i1, sininen, pallo ja ei
  - esine: i4, sininen, pallo ja kyllä
  - esine: i5, vihreä, kuutio ja kyllä
  - esine: i2, vihreä, kuutio ja ei
  - esine: i0, vihreä, kuutio ja kyllä
  - esine: i6, punainen, kartio ja kyllä
  - esine: i3, punainen, pallo ja kyllä
  - esine: i7, sininen, pallo ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - 预测是若：theory3、形状和立方体
  - 预测是若：theory1、颜色和蓝色的
  - 预测是若：theory1、颜色和蓝色的
  - theory2的理论规模是2
  - 预测是若：theory3、形状和圆锥
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory0、颜色和绿色的
  - theory3的理论规模是4
  - 预测是若：theory2、颜色和蓝色的
  - theory0的理论规模是4
  - theory1的理论规模是4
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i1、蓝色的、球体和否
  - 的物品是：i4、蓝色的、球体和是
  - 的物品是：i5、绿色的、立方体和是
  - 的物品是：i2、绿色的、立方体和否
  - 的物品是：i0、绿色的、立方体和是
  - 的物品是：i6、红色的、圆锥和是
  - 的物品是：i3、红色的、球体和是
  - 的物品是：i7、蓝色的、球体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - 予言するはいもし...:theory3, 形と立方体
  - 予言するはいもし...:theory1, 色と青い
  - 予言するはいもし...:theory1, 色と青い
  - theory22理論大きさ
  - 予言するはいもし...:theory3, 形と松毬
  - 予言するはいもし...:theory0, 色と赤い
  - 予言するはいもし...:theory0, 色と緑の
  - theory34理論大きさ
  - 予言するはいもし...:theory2, 色と青い
  - theory04理論大きさ
  - theory14理論大きさ
費用モデル:例外費やす:4.
資料:
  - 項目:i1, 青い, 球といいえ
  - 項目:i4, 青い, 球とはい
  - 項目:i5, 緑の, 立方体とはい
  - 項目:i2, 緑の, 立方体といいえ
  - 項目:i0, 緑の, 立方体とはい
  - 項目:i6, 赤い, 松毬とはい
  - 項目:i3, 赤い, 球とはい
  - 項目:i7, 青い, 球とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - öngörür evet eğer: theory3, şekil ve küp
  - öngörür evet eğer: theory1, renk ve mavi
  - öngörür evet eğer: theory1, renk ve mavi
  - theory2 2 kuram boyut
  - öngörür evet eğer: theory3, şekil ve koni
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory0, renk ve yeşil
  - theory3 4 kuram boyut
  - öngörür evet eğer: theory2, renk ve mavi
  - theory0 4 kuram boyut
  - theory1 4 kuram boyut
Cost model: exception maliyet: 4.
Veriler:
  - item: i1, mavi, küre ve hayır
  - item: i4, mavi, küre ve evet
  - item: i5, yeşil, küp ve evet
  - item: i2, yeşil, küp ve hayır
  - item: i0, yeşil, küp ve evet
  - item: i6, kırmızı, koni ve evet
  - item: i3, kırmızı, küre ve evet
  - item: i7, mavi, küre ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كَعْبَة
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَزْرَق
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَزْرَق
  - نَظَرِيَّة مَقَاس theory2 2
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory3 4
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَزْرَق
  - نَظَرِيَّة مَقَاس theory0 4
  - نَظَرِيَّة مَقَاس theory1 4
cost model: اِسْتِثْنَاء cost 4.
بَيَانَات:
  - بَنْد: i1، أَزْرَق، كُرَة وَـ no
  - بَنْد: i4، أَزْرَق، كُرَة وَـ نَعَم
  - بَنْد: i5، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i2، أَخْضَر، كَعْبَة وَـ no
  - بَنْد: i0، أَخْضَر، كَعْبَة وَـ نَعَم
  - بَنْد: i6، أَحْمَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i3، أَحْمَر، كُرَة وَـ نَعَم
  - بَنْد: i7، أَزْرَق، كُرَة وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - predict დიახ თუ: theory3, ფორმა და კუბი
  - predict დიახ თუ: theory1, ფერი და ლურჯი
  - predict დიახ თუ: theory1, ფერი და ლურჯი
  - theory2 2 თეორია ზომა
  - predict დიახ თუ: theory3, ფორმა და გირჩა
  - predict დიახ თუ: theory0, ფერი და წითელი
  - predict დიახ თუ: theory0, ფერი და მწვანე
  - theory3 4 თეორია ზომა
  - predict დიახ თუ: theory2, ფერი და ლურჯი
  - theory0 4 თეორია ზომა
  - theory1 4 თეორია ზომა
Ფასი მოდელი: გამონაკლისი ღირს 4.
Მონაცემები:
  - item: i1, ლურჯი, სფერო და no
  - item: i4, ლურჯი, სფერო და დიახ
  - item: i5, მწვანე, კუბი და დიახ
  - item: i2, მწვანე, კუბი და no
  - item: i0, მწვანე, კუბი და დიახ
  - item: i6, წითელი, გირჩა და დიახ
  - item: i3, წითელი, სფერო და დიახ
  - item: i7, ლურჯი, სფერო და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - matakite āe mēnā: theory3, āhuahanga me mataono
  - matakite āe mēnā: theory1, kano me kahurangi
  - matakite āe mēnā: theory1, kano me kahurangi
  - ariā size theory2 2
  - matakite āe mēnā: theory3, āhuahanga me koeko
  - matakite āe mēnā: theory0, kano me red
  - matakite āe mēnā: theory0, kano me kākāriki
  - ariā size theory3 4
  - matakite āe mēnā: theory2, kano me kahurangi
  - ariā size theory0 4
  - ariā size theory1 4
Cost model: hāungatanga cost 4.
Hōtuku:
  - tūemi: i1, kahurangi, poi me kāo
  - tūemi: i4, kahurangi, poi me āe
  - tūemi: i5, kākāriki, mataono me āe
  - tūemi: i2, kākāriki, mataono me kāo
  - tūemi: i0, kākāriki, mataono me āe
  - tūemi: i6, red, koeko me āe
  - tūemi: i3, red, poi me āe
  - tūemi: i7, kahurangi, poi me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - dự báo vâng nếu: theory3, hình dáng và lập phương
  - dự báo vâng nếu: theory1, màu và blue
  - dự báo vâng nếu: theory1, màu và blue
  - theory2 lý thuyết kích thước 2
  - dự báo vâng nếu: theory3, hình dáng và nón
  - dự báo vâng nếu: theory0, màu và đỏ
  - dự báo vâng nếu: theory0, màu và green
  - theory3 lý thuyết kích thước 4
  - dự báo vâng nếu: theory2, màu và blue
  - theory0 lý thuyết kích thước 4
  - theory1 lý thuyết kích thước 4
Cost model: lệ ngoại cost 4.
Dữ liệu:
  - vật phẩm: i1, blue, mặt cầu và no
  - vật phẩm: i4, blue, mặt cầu và vâng
  - vật phẩm: i5, green, lập phương và vâng
  - vật phẩm: i2, green, lập phương và no
  - vật phẩm: i0, green, lập phương và vâng
  - vật phẩm: i6, đỏ, nón và vâng
  - vật phẩm: i3, đỏ, mặt cầu và vâng
  - vật phẩm: i7, blue, mặt cầu và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและลูกบาศก์
  - ทำนายใช่ถ้า หาก:theory1, สีและน้ำเงิน
  - ทำนายใช่ถ้า หาก:theory1, สีและน้ำเงิน
  - theory2ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory0, สีและแดง
  - ทำนายใช่ถ้า หาก:theory0, สีและเขียว
  - theory3ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory2, สีและน้ำเงิน
  - theory0ทฤษฎีขนาด4
  - theory1ทฤษฎีขนาด4
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:4.
ข้อมูล:
  - item:i1, น้ำเงิน, ทรงกลมและไฮ้
  - item:i4, น้ำเงิน, ทรงกลมและใช่
  - item:i5, เขียว, ลูกบาศก์และใช่
  - item:i2, เขียว, ลูกบาศก์และไฮ้
  - item:i0, เขียว, ลูกบาศก์และใช่
  - item:i6, แดง, กรวยและใช่
  - item:i3, แดง, ทรงกลมและใช่
  - item:i7, น้ำเงิน, ทรงกลมและใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - கணி ஆம் என்றால்: theory3, shape மற்றும் cube
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் நீலம்
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் நீலம்
  - theory2 2 theory size
  - கணி ஆம் என்றால்: theory3, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் பச்சை
  - theory3 4 theory size
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் நீலம்
  - theory0 4 theory size
  - theory1 4 theory size
cost model: exception செலவு 4.
தரவுகள்:
  - item: i1, நீலம், sphere மற்றும் no
  - item: i4, நீலம், sphere மற்றும் ஆம்
  - item: i5, பச்சை, cube மற்றும் ஆம்
  - item: i2, பச்சை, cube மற்றும் no
  - item: i0, பச்சை, cube மற்றும் ஆம்
  - item: i6, சிவப்பு, cone மற்றும் ஆம்
  - item: i3, சிவப்பு, sphere மற்றும் ஆம்
  - item: i7, நீலம், sphere மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - predict ndiyo kama: theory3, umbo na mchemraba
  - predict ndiyo kama: theory1, rangi na buluu
  - predict ndiyo kama: theory1, rangi na buluu
  - theory2 theory size 2
  - predict ndiyo kama: theory3, umbo na koni
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory0, rangi na kijani
  - theory3 theory size 4
  - predict ndiyo kama: theory2, rangi na buluu
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception gharimu: 4.
Data:
  - item: i1, buluu, tufe na hapana
  - item: i4, buluu, tufe na ndiyo
  - item: i5, kijani, mchemraba na ndiyo
  - item: i2, kijani, mchemraba na hapana
  - item: i0, kijani, mchemraba na ndiyo
  - item: i6, ekundu, koni na ndiyo
  - item: i3, ekundu, tufe na ndiyo
  - item: i7, buluu, tufe na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó cube
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dootłʼizh
  - theory2 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory3 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dootłʼizh
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception bą́ą́h ílį́ 4.
Data:
  - item: i1, dootłʼizh, sphere dóó no
  - item: i4, dootłʼizh, sphere dóó aooʼ
  - item: i5, dinootłʼizh, cube dóó aooʼ
  - item: i2, dinootłʼizh, cube dóó no
  - item: i0, dinootłʼizh, cube dóó aooʼ
  - item: i6, łichííʼ, cone dóó aooʼ
  - item: i3, łichííʼ, sphere dóó aooʼ
  - item: i7, dootłʼizh, sphere dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - predict aap if: theory3, shape aamma cube
  - predict aap if: theory1, qalipaat aamma tungujortoq
  - predict aap if: theory1, qalipaat aamma tungujortoq
  - theory2 2 theory size
  - predict aap if: theory3, shape aamma cone
  - predict aap if: theory0, qalipaat aamma aappalajaarpoq
  - predict aap if: theory0, qalipaat aamma qorsuk
  - theory3 4 theory size
  - predict aap if: theory2, qalipaat aamma tungujortoq
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, tungujortoq, sphere aamma no
  - item: i4, tungujortoq, sphere aamma aap
  - item: i5, qorsuk, cube aamma aap
  - item: i2, qorsuk, cube aamma no
  - item: i0, qorsuk, cube aamma aap
  - item: i6, aappalajaarpoq, cone aamma aap
  - item: i3, aappalajaarpoq, sphere aamma aap
  - item: i7, tungujortoq, sphere aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ya sapos: theory3, shape na cube
  - predict ya sapos: theory1, kala na blu
  - predict ya sapos: theory1, kala na blu
  - theory2 theory size 2
  - predict ya sapos: theory3, shape na cone
  - predict ya sapos: theory0, kala na ret
  - predict ya sapos: theory0, kala na grinpela
  - theory3 theory size 4
  - predict ya sapos: theory2, kala na blu
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blu, sphere na no
  - item: i4, blu, sphere na ya
  - item: i5, grinpela, cube na ya
  - item: i2, grinpela, cube na no
  - item: i0, grinpela, cube na ya
  - item: i6, ret, cone na ya
  - item: i3, ret, sphere na ya
  - item: i7, blu, sphere na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, color blue
  - predict yes if: theory1, color blue
  - theory2 theory size 2
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color màxke
  - predict yes if: theory0, color green
  - theory3 theory size 4
  - predict yes if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere yes
  - item: i5, green, cube yes
  - item: i2, green, cube no
  - item: i0, green, cube yes
  - item: i6, màxke, cone yes
  - item: i3, màxke, sphere yes
  - item: i7, blue, sphere yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - predict yes if: theory3, shape а cube
  - predict yes if: theory1, бос а blue
  - predict yes if: theory1, бос а blue
  - theory2 theory size 2
  - predict yes if: theory3, shape а cone
  - predict yes if: theory0, бос а цӏе
  - predict yes if: theory0, бос а баьццара
  - theory3 theory size 4
  - predict yes if: theory2, бос а blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere а no
  - item: i4, blue, sphere а yes
  - item: i5, баьццара, cube а yes
  - item: i2, баьццара, cube а no
  - item: i0, баьццара, cube а yes
  - item: i6, цӏе, cone а yes
  - item: i3, цӏе, sphere а yes
  - item: i7, blue, sphere а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - 예언하다 네 만약: theory3, 모양 정육면체
  - 예언하다 네 만약: theory1, 색깔 푸르다
  - 예언하다 네 만약: theory1, 색깔 푸르다
  - theory2 2 이론 크기
  - 예언하다 네 만약: theory3, 모양 솔방울
  - 예언하다 네 만약: theory0, 색깔 빨갛다
  - 예언하다 네 만약: theory0, 색깔 녹색의
  - theory3 4 이론 크기
  - 예언하다 네 만약: theory2, 색깔 푸르다
  - theory0 4 이론 크기
  - theory1 4 이론 크기
cost model: 예외 cost 4.
자료:
  - 아이템: i1, 푸르다, 구체 아니요
  - 아이템: i4, 푸르다, 구체 네
  - 아이템: i5, 녹색의, 정육면체 네
  - 아이템: i2, 녹색의, 정육면체 아니요
  - 아이템: i0, 녹색의, 정육면체 네
  - 아이템: i6, 빨갛다, 솔방울 네
  - 아이템: i3, 빨갛다, 구체 네
  - 아이템: i7, 푸르다, 구체 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - aurresan bai if: theory3, shape eta kubo
  - aurresan bai if: theory1, kolore eta urdin
  - aurresan bai if: theory1, kolore eta urdin
  - theory2 2 theory tamaina
  - aurresan bai if: theory3, shape eta pinaburu
  - aurresan bai if: theory0, kolore eta gorri
  - aurresan bai if: theory0, kolore eta berde
  - theory3 4 theory tamaina
  - aurresan bai if: theory2, kolore eta urdin
  - theory0 4 theory tamaina
  - theory1 4 theory tamaina
Cost model: exception kostatu 4.
Data:
  - item: i1, urdin, esfera eta no
  - item: i4, urdin, esfera eta bai
  - item: i5, berde, kubo eta bai
  - item: i2, berde, kubo eta no
  - item: i0, berde, kubo eta bai
  - item: i6, gorri, pinaburu eta bai
  - item: i3, gorri, esfera eta bai
  - item: i7, urdin, esfera eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - predict ᎥᎥ if: theory3, shape ᎠᎴ cube
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - theory2 theory size 2
  - predict ᎥᎥ if: theory3, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory3 theory size 4
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i4, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᎥᎥ
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i2, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᏝᏃ
  - item: i0, ᎢᏤ ᎢᏳᏍᏗ, cube ᎠᎴ ᎥᎥ
  - item: i6, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
  - item: i3, ᎩᎦᎨ, sphere ᎠᎴ ᎥᎥ
  - item: i7, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict arí if: theory3, shape cube
  - predict arí if: theory1, llimphi anqas
  - predict arí if: theory1, llimphi anqas
  - theory2 theory chhika 2
  - predict arí if: theory3, shape cone
  - predict arí if: theory0, llimphi puka
  - predict arí if: theory0, llimphi waylla
  - theory3 theory chhika 4
  - predict arí if: theory2, llimphi anqas
  - theory0 theory chhika 4
  - theory1 theory chhika 4
Cost model: exception cost 4.
Data:
  - item: i1, anqas, sphere mánan
  - item: i4, anqas, sphere arí
  - item: i5, waylla, cube arí
  - item: i2, waylla, cube mánan
  - item: i0, waylla, cube arí
  - item: i6, puka, cone arí
  - item: i3, puka, sphere arí
  - item: i7, anqas, sphere arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape ыкӏи cube
  - predict yes if: theory1, color ыкӏи blue
  - predict yes if: theory1, color ыкӏи blue
  - theory2 2 тиориэ size
  - predict yes if: theory3, shape ыкӏи cone
  - predict yes if: theory0, color ыкӏи плъыжьы
  - predict yes if: theory0, color ыкӏи уцышъо
  - theory3 4 тиориэ size
  - predict yes if: theory2, color ыкӏи blue
  - theory0 4 тиориэ size
  - theory1 4 тиориэ size
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere ыкӏи no
  - item: i4, blue, sphere ыкӏи yes
  - item: i5, уцышъо, cube ыкӏи yes
  - item: i2, уцышъо, cube ыкӏи no
  - item: i0, уцышъо, cube ыкӏи yes
  - item: i6, плъыжьы, cone ыкӏи yes
  - item: i3, плъыжьы, sphere ыкӏи yes
  - item: i7, blue, sphere ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - predict yes if: theory3, shape thiab cube
  - predict yes if: theory1, xim thiab xiav
  - predict yes if: theory1, xim thiab xiav
  - theory2 theory size 2
  - predict yes if: theory3, shape thiab cone
  - predict yes if: theory0, xim thiab liab
  - predict yes if: theory0, xim thiab ntsuab
  - theory3 theory size 4
  - predict yes if: theory2, xim thiab xiav
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception nqi 4.
Data:
  - item: i1, xiav, sphere thiab no
  - item: i4, xiav, sphere thiab yes
  - item: i5, ntsuab, cube thiab yes
  - item: i2, ntsuab, cube thiab no
  - item: i0, ntsuab, cube thiab yes
  - item: i6, liab, cone thiab yes
  - item: i3, liab, sphere thiab yes
  - item: i7, xiav, sphere thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - predict ngai if: theory3, shape cube
  - predict ngai if: theory1, color blue
  - predict ngai if: theory1, color blue
  - theory2 theory size 2
  - predict ngai if: theory3, shape cone
  - predict ngai if: theory0, color red
  - predict ngai if: theory0, color green
  - theory3 theory size 4
  - predict ngai if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere woi
  - item: i4, blue, sphere ngai
  - item: i5, green, cube ngai
  - item: i2, green, cube woi
  - item: i0, green, cube ngai
  - item: i6, red, cone ngai
  - item: i3, red, sphere ngai
  - item: i7, blue, sphere ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - predict i̠i if: theory3, shape micha' cube
  - predict i̠i if: theory1, color micha' blue
  - predict i̠i if: theory1, color micha' blue
  - theory2 theory size 2
  - predict i̠i if: theory3, shape micha' cone
  - predict i̠i if: theory0, color micha' homa
  - predict i̠i if: theory0, color micha' green
  - theory3 theory size 4
  - predict i̠i if: theory2, color micha' blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere micha' no
  - item: i4, blue, sphere micha' i̠i
  - item: i5, green, cube micha' i̠i
  - item: i2, green, cube micha' no
  - item: i0, green, cube micha' i̠i
  - item: i6, homa, cone micha' i̠i
  - item: i3, homa, sphere micha' i̠i
  - item: i7, blue, sphere micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - predict yes if: theory3, shape カ cube
  - predict yes if: theory1, color カ blue
  - predict yes if: theory1, color カ blue
  - theory2 2 theory size
  - predict yes if: theory3, shape カ cone
  - predict yes if: theory0, color カ フレ
  - predict yes if: theory0, color カ green
  - theory3 4 theory size
  - predict yes if: theory2, color カ blue
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere カ ソモ
  - item: i4, blue, sphere カ yes
  - item: i5, green, cube カ yes
  - item: i2, green, cube カ ソモ
  - item: i0, green, cube カ yes
  - item: i6, フレ, cone カ yes
  - item: i3, フレ, sphere カ yes
  - item: i7, blue, sphere カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes эвыр: theory3, shape ынкъам cube
  - predict yes эвыр: theory1, пэран ынкъам ныԓиԓиԓӄин
  - predict yes эвыр: theory1, пэран ынкъам ныԓиԓиԓӄин
  - theory2 theory size 2
  - predict yes эвыр: theory3, shape ынкъам cone
  - predict yes эвыр: theory0, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory0, пэран ынкъам green
  - theory3 theory size 4
  - predict yes эвыр: theory2, пэран ынкъам ныԓиԓиԓӄин
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i4, ныԓиԓиԓӄин, sphere ынкъам yes
  - item: i5, green, cube ынкъам yes
  - item: i2, green, cube ынкъам no
  - item: i0, green, cube ынкъам yes
  - item: i6, чеԓгыԓьын, cone ынкъам yes
  - item: i3, чеԓгыԓьын, sphere ынкъам yes
  - item: i7, ныԓиԓиԓӄин, sphere ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes wa: theory3, shape yéetel cube
  - predict yes wa: theory1, color yéetel blue
  - predict yes wa: theory1, color yéetel blue
  - theory2 theory size 2
  - predict yes wa: theory3, shape yéetel cone
  - predict yes wa: theory0, color yéetel chak
  - predict yes wa: theory0, color yéetel yaʼax
  - theory3 theory size 4
  - predict yes wa: theory2, color yéetel blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere yéetel no
  - item: i4, blue, sphere yéetel yes
  - item: i5, yaʼax, cube yéetel yes
  - item: i2, yaʼax, cube yéetel no
  - item: i0, yaʼax, cube yéetel yes
  - item: i6, chak, cone yéetel yes
  - item: i3, chak, sphere yéetel yes
  - item: i7, blue, sphere yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict hą if: theory3, shape cube
  - predict hą if: theory1, color blue
  - predict hą if: theory1, color blue
  - theory2 theory size 2
  - predict hą if: theory3, shape cone
  - predict hą if: theory0, color red
  - predict hą if: theory0, color green
  - theory3 theory size 4
  - predict hą if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere hą
  - item: i5, green, cube hą
  - item: i2, green, cube no
  - item: i0, green, cube hą
  - item: i6, red, cone hą
  - item: i3, red, sphere hą
  - item: i7, blue, sphere hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - predict yes if: theory3, shape na cube
  - predict yes if: theory1, color na blue
  - predict yes if: theory1, color na blue
  - theory2 2 theory size
  - predict yes if: theory3, shape na cone
  - predict yes if: theory0, color na red
  - predict yes if: theory0, color na green
  - theory3 4 theory size
  - predict yes if: theory2, color na blue
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere na no
  - item: i4, blue, sphere na yes
  - item: i5, green, cube na yes
  - item: i2, green, cube na no
  - item: i0, green, cube na yes
  - item: i6, red, cone na yes
  - item: i3, red, sphere na yes
  - item: i7, blue, sphere na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict heẽ if: theory3, shape cube
  - predict heẽ if: theory1, color blue
  - predict heẽ if: theory1, color blue
  - theory2 theory size 2
  - predict heẽ if: theory3, shape cone
  - predict heẽ if: theory0, color pytã
  - predict heẽ if: theory0, color green
  - theory3 theory size 4
  - predict heẽ if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere nahániri
  - item: i4, blue, sphere heẽ
  - item: i5, green, cube heẽ
  - item: i2, green, cube nahániri
  - item: i0, green, cube heẽ
  - item: i6, pytã, cone heẽ
  - item: i3, pytã, sphere heẽ
  - item: i7, blue, sphere heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict jisa if: theory3, shape cube
  - predict jisa if: theory1, sami larama
  - predict jisa if: theory1, sami larama
  - theory2 2 theory size
  - predict jisa if: theory3, shape cone
  - predict jisa if: theory0, sami chupika
  - predict jisa if: theory0, sami ch’uxña
  - theory3 4 theory size
  - predict jisa if: theory2, sami larama
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, larama, sphere no
  - item: i4, larama, sphere jisa
  - item: i5, ch’uxña, cube jisa
  - item: i2, ch’uxña, cube no
  - item: i0, ch’uxña, cube jisa
  - item: i6, chupika, cone jisa
  - item: i3, chupika, sphere jisa
  - item: i7, larama, sphere jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ʻée if: theory3, shape ǂʻá cube
  - predict ʻée if: theory1, color ǂʻá blue
  - predict ʻée if: theory1, color ǂʻá blue
  - theory2 theory ʻǁnàhã 2
  - predict ʻée if: theory3, shape ǂʻá cone
  - predict ʻée if: theory0, color ǂʻá red
  - predict ʻée if: theory0, color ǂʻá green
  - theory3 theory ʻǁnàhã 4
  - predict ʻée if: theory2, color ǂʻá blue
  - theory0 theory ʻǁnàhã 4
  - theory1 theory ʻǁnàhã 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere ǂʻá ʻĩĩ
  - item: i4, blue, sphere ǂʻá ʻée
  - item: i5, green, cube ǂʻá ʻée
  - item: i2, green, cube ǂʻá ʻĩĩ
  - item: i0, green, cube ǂʻá ʻée
  - item: i6, red, cone ǂʻá ʻée
  - item: i3, red, sphere ǂʻá ʻée
  - item: i7, blue, sphere ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, вал blue
  - predict yes if: theory1, вал blue
  - theory2 2 theory size
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, вал red
  - predict yes if: theory0, вал green
  - theory3 4 theory size
  - predict yes if: theory2, вал blue
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere yes
  - item: i5, green, cube yes
  - item: i2, green, cube no
  - item: i0, green, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, color blue
  - predict yes if: theory1, color blue
  - theory2 theory size 2
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - theory3 theory size 4
  - predict yes if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere yes
  - item: i5, green, cube yes
  - item: i2, green, cube no
  - item: i0, green, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, color blue
  - predict yes if: theory1, color blue
  - theory2 2 theory size
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - theory3 4 theory size
  - predict yes if: theory2, color blue
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere yes
  - item: i5, green, cube yes
  - item: i2, green, cube no
  - item: i0, green, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ee if: theory3, shape cube
  - predict ee if: theory1, color blue
  - predict ee if: theory1, color blue
  - theory2 theory size 2
  - predict ee if: theory3, shape cone
  - predict ee if: theory0, color red
  - predict ee if: theory0, color green
  - theory3 theory size 4
  - predict ee if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere ee
  - item: i5, green, cube ee
  - item: i2, green, cube no
  - item: i0, green, cube ee
  - item: i6, red, cone ee
  - item: i3, red, sphere ee
  - item: i7, blue, sphere ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, color blue
  - predict yes if: theory1, color blue
  - theory2 theory size 2
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - theory3 theory size 4
  - predict yes if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere inte
  - item: i4, blue, sphere yes
  - item: i5, green, cube yes
  - item: i2, green, cube inte
  - item: i0, green, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - prédire oui si: theory3, forme et cube
  - prédire oui si: theory1, couleur et bleu
  - prédire oui si: theory1, couleur et bleu
  - theory2 théorie taille 2
  - prédire oui si: theory3, forme et cône
  - prédire oui si: theory0, couleur et rouge
  - prédire oui si: theory0, couleur et vert
  - theory3 théorie taille 4
  - prédire oui si: theory2, couleur et bleu
  - theory0 théorie taille 4
  - theory1 théorie taille 4
Coûts : exception coûter 4.
Les données sont:
  - article: i1, bleu, sphère et no
  - article: i4, bleu, sphère et oui
  - article: i5, vert, cube et oui
  - article: i2, vert, cube et no
  - article: i0, vert, cube et oui
  - article: i6, rouge, cône et oui
  - article: i3, rouge, sphère et oui
  - article: i7, bleu, sphère et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - predict jes se: theory3, formi kaj kubo
  - predict jes se: theory1, koloro kaj blua
  - predict jes se: theory1, koloro kaj blua
  - theory2 teorio grandeco 2
  - predict jes se: theory3, formi kaj strobilo
  - predict jes se: theory0, koloro kaj ruĝa
  - predict jes se: theory0, koloro kaj verda
  - theory3 teorio grandeco 4
  - predict jes se: theory2, koloro kaj blua
  - theory0 teorio grandeco 4
  - theory1 teorio grandeco 4
Kosto pozistino: escepto kosti 4.
Datumo:
  - ero: i1, blua, sfero kaj no
  - ero: i4, blua, sfero kaj jes
  - ero: i5, verda, kubo kaj jes
  - ero: i2, verda, kubo kaj no
  - ero: i0, verda, kubo kaj jes
  - ero: i6, ruĝa, strobilo kaj jes
  - ero: i3, ruĝa, sfero kaj jes
  - ero: i7, blua, sfero kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - megjósol igen ha: theory3, alak és kocka
  - megjósol igen ha: theory1, szín és kék
  - megjósol igen ha: theory1, szín és kék
  - theory2 elmélet méret 2
  - megjósol igen ha: theory3, alak és toboz
  - megjósol igen ha: theory0, szín és piros
  - megjósol igen ha: theory0, szín és zöld
  - theory3 elmélet méret 4
  - megjósol igen ha: theory2, szín és kék
  - theory0 elmélet méret 4
  - theory1 elmélet méret 4
Költség manöken: kivétel kerül 4.
Adat:
  - cikk: i1, kék, gömb és no
  - cikk: i4, kék, gömb és igen
  - cikk: i5, zöld, kocka és igen
  - cikk: i2, zöld, kocka és no
  - cikk: i0, zöld, kocka és igen
  - cikk: i6, piros, toboz és igen
  - cikk: i3, piros, gömb és igen
  - cikk: i7, kék, gömb és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - predict係要是:theory3, 形狀及cube
  - predict係要是:theory1, 色及藍色嘅
  - predict係要是:theory1, 色及藍色嘅
  - theory2theory size2
  - predict係要是:theory3, 形狀及cone
  - predict係要是:theory0, 色及紅
  - predict係要是:theory0, 色及綠色嘅
  - theory3theory size4
  - predict係要是:theory2, 色及藍色嘅
  - theory0theory size4
  - theory1theory size4
cost model:exception cost:4.
數據:
  - item:i1, 藍色嘅, sphere及no
  - item:i4, 藍色嘅, sphere及係
  - item:i5, 綠色嘅, cube及係
  - item:i2, 綠色嘅, cube及no
  - item:i0, 綠色嘅, cube及係
  - item:i6, 紅, cone及係
  - item:i3, 紅, sphere及係
  - item:i7, 藍色嘅, sphere及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - predict yes if: theory3, shape とぅ cube
  - predict yes if: theory1, color とぅ blue
  - predict yes if: theory1, color とぅ blue
  - theory2 theory size 2
  - predict yes if: theory3, shape とぅ cone
  - predict yes if: theory0, color とぅ red
  - predict yes if: theory0, color とぅ green
  - theory3 theory size 4
  - predict yes if: theory2, color とぅ blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere とぅ no
  - item: i4, blue, sphere とぅ yes
  - item: i5, green, cube とぅ yes
  - item: i2, green, cube とぅ no
  - item: i0, green, cube とぅ yes
  - item: i6, red, cone とぅ yes
  - item: i3, red, sphere とぅ yes
  - item: i7, blue, sphere とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - predict иә егер: theory3, нысан және куб
  - predict иә егер: theory1, түс және көк
  - predict иә егер: theory1, түс және көк
  - theory2 теория мөлшер 2
  - predict иә егер: theory3, нысан және бүршік
  - predict иә егер: theory0, түс және қызыл
  - predict иә егер: theory0, түс және жасыл
  - theory3 теория мөлшер 4
  - predict иә егер: theory2, түс және көк
  - theory0 теория мөлшер 4
  - theory1 теория мөлшер 4
Тұру үлгі: ерекшелік тұру 4.
Деректер:
  - зат: i1, көк, сфера және жоқ
  - зат: i4, көк, сфера және иә
  - зат: i5, жасыл, куб және иә
  - зат: i2, жасыл, куб және жоқ
  - зат: i0, жасыл, куб және иә
  - зат: i6, қызыл, бүршік және иә
  - зат: i3, қызыл, сфера және иә
  - зат: i7, көк, сфера және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - predict כֵּן אִם: theory3, צוּרָה וְ־ קובייה
  - predict כֵּן אִם: theory1, צֶבַע וְ־ כָּחוֹל
  - predict כֵּן אִם: theory1, צֶבַע וְ־ כָּחוֹל
  - theory2 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory3, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory0, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory0, צֶבַע וְ־ ירוק מקנאה
  - theory3 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory2, צֶבַע וְ־ כָּחוֹל
  - theory0 תּוֹרָה גֹּדֶל 4
  - theory1 תּוֹרָה גֹּדֶל 4
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 4.
נְתוּנִים:
  - אבזר: i1, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i4, כָּחוֹל, כַּדּוּר וְ־ כֵּן
  - אבזר: i5, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i2, ירוק מקנאה, קובייה וְ־ no
  - אבזר: i0, ירוק מקנאה, קובייה וְ־ כֵּן
  - אבזר: i6, אָדוֹם, אצטרובל וְ־ כֵּן
  - אבזר: i3, אָדוֹם, כַּדּוּר וְ־ כֵּן
  - אבזר: i7, כָּחוֹל, כַּדּוּר וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - predict იახ if: theory3, shape დო cube
  - predict იახ if: theory1, color დო blue
  - predict იახ if: theory1, color დო blue
  - theory2 theory size 2
  - predict იახ if: theory3, shape დო cone
  - predict იახ if: theory0, color დო red
  - predict იახ if: theory0, color დო green
  - theory3 theory size 4
  - predict იახ if: theory2, color დო blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere დო no
  - item: i4, blue, sphere დო იახ
  - item: i5, green, cube დო იახ
  - item: i2, green, cube დო no
  - item: i0, green, cube დო იახ
  - item: i6, red, cone დო იახ
  - item: i3, red, sphere დო იახ
  - item: i7, blue, sphere დო იახ
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - predict ya jika: theory3, bentuk dan kubus
  - predict ya jika: theory1, warna dan biru
  - predict ya jika: theory1, warna dan biru
  - theory2 teori ukuran 2
  - predict ya jika: theory3, bentuk dan kerucut
  - predict ya jika: theory0, warna dan merah
  - predict ya jika: theory0, warna dan hijau
  - theory3 teori ukuran 4
  - predict ya jika: theory2, warna dan biru
  - theory0 teori ukuran 4
  - theory1 teori ukuran 4
Cost model: pengecualian menghabiskan biaya 4.
Data:
  - benda: i1, biru, bola dan no
  - benda: i4, biru, bola dan ya
  - benda: i5, hijau, kubus dan ya
  - benda: i2, hijau, kubus dan no
  - benda: i0, hijau, kubus dan ya
  - benda: i6, merah, kerucut dan ya
  - benda: i3, merah, bola dan ya
  - benda: i7, biru, bola dan ya
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគូប
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងខៀវ
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងខៀវ
  - theory2ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងកោណ
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងបៃតង
  - theory3ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងខៀវ
  - theory0ទ្រឹស្ដីទំហំ4
  - theory1ទ្រឹស្ដីទំហំ4
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:4.
ទិន្នន័យ:
  - item:i1, ខៀវ, គោលនឹងទេ
  - item:i4, ខៀវ, គោលនឹងច៎ា
  - item:i5, បៃតង, គូបនឹងច៎ា
  - item:i2, បៃតង, គូបនឹងទេ
  - item:i0, បៃតង, គូបនឹងច៎ា
  - item:i6, ក្រហម, កោណនឹងច៎ា
  - item:i3, ក្រហម, គោលនឹងច៎ា
  - item:i7, ខៀវ, គោលនឹងច៎ា
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະລູກບາດ
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຟ້າ
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຟ້າ
  - theory2ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະcone
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຂຽວ
  - theory3ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຟ້າ
  - theory0ທິດສະດີຂະຫນາດ4
  - theory1ທິດສະດີຂະຫນາດ4
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:4.
ຂໍ້ມູນ:
  - item:i1, ຟ້າ, ຄະດີແລະno
  - item:i4, ຟ້າ, ຄະດີແລະແມ່ນ
  - item:i5, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i2, ຂຽວ, ລູກບາດແລະno
  - item:i0, ຂຽວ, ລູກບາດແລະແມ່ນ
  - item:i6, ແດງ, coneແລະແມ່ນ
  - item:i3, ແດງ, ຄະດີແລະແມ່ນ
  - item:i7, ຟ້າ, ຄະດີແລະແມ່ນ
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - predict అవును అయితే: theory3, ఆకారం మరియు cube
  - predict అవును అయితే: theory1, రంగు మరియు నీలము
  - predict అవును అయితే: theory1, రంగు మరియు నీలము
  - theory2 2 theory size
  - predict అవును అయితే: theory3, ఆకారం మరియు cone
  - predict అవును అయితే: theory0, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory0, రంగు మరియు పచ్చని
  - theory3 4 theory size
  - predict అవును అయితే: theory2, రంగు మరియు నీలము
  - theory0 4 theory size
  - theory1 4 theory size
cost model: exception cost 4.
data:
  - item: i1, నీలము, గోళము మరియు no
  - item: i4, నీలము, గోళము మరియు అవును
  - item: i5, పచ్చని, cube మరియు అవును
  - item: i2, పచ్చని, cube మరియు no
  - item: i0, పచ్చని, cube మరియు అవును
  - item: i6, ఎర్ర, cone మరియు అవును
  - item: i3, ఎర్ర, గోళము మరియు అవును
  - item: i7, నీలము, గోళము మరియు అవును
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - predict hẹn if: theory3, shape cube
  - predict hẹn if: theory1, color dúdú
  - predict hẹn if: theory1, color dúdú
  - theory2 theory size 2
  - predict hẹn if: theory3, shape cone
  - predict hẹn if: theory0, color pupa
  - predict hẹn if: theory0, color green
  - theory3 theory size 4
  - predict hẹn if: theory2, color dúdú
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, dúdú, sphere rárá
  - item: i4, dúdú, sphere bẹ́ẹ̀ ni
  - item: i5, green, cube bẹ́ẹ̀ ni
  - item: i2, green, cube rárá
  - item: i0, green, cube bẹ́ẹ̀ ni
  - item: i6, pupa, cone bẹ́ẹ̀ ni
  - item: i3, pupa, sphere bẹ́ẹ̀ ni
  - item: i7, dúdú, sphere bẹ́ẹ̀ ni
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict au if: theory3, shape cube
  - predict au if: theory1, color blue
  - predict au if: theory1, color blue
  - theory2 2 theory size
  - predict au if: theory3, shape cone
  - predict au if: theory0, color łichíí
  - predict au if: theory0, color green
  - theory3 4 theory size
  - predict au if: theory2, color blue
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere au
  - item: i5, green, cube au
  - item: i2, green, cube no
  - item: i0, green, cube au
  - item: i6, łichíí, cone au
  - item: i3, łichíí, sphere au
  - item: i7, blue, sphere au
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, color blue
  - predict yes if: theory1, color blue
  - theory2 theory size 2
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - theory3 theory size 4
  - predict yes if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
cost model: exception cost 4.
data:
  - item: i1, blue, sphere ᐋᒃᑲ
  - item: i4, blue, sphere yes
  - item: i5, green, cube yes
  - item: i2, green, cube ᐋᒃᑲ
  - item: i0, green, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blue, sphere yes
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, color blaw
  - predict yes if: theory1, color blaw
  - theory2 theory size 2
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color red
  - predict yes if: theory0, color grun
  - theory3 theory size 4
  - predict yes if: theory2, color blaw
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blaw, sphere no
  - item: i4, blaw, sphere yes
  - item: i5, grun, cube yes
  - item: i2, grun, cube no
  - item: i0, grun, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blaw, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, color blue
  - predict yes if: theory1, color blue
  - theory2 theory size 2
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - theory3 theory size 4
  - predict yes if: theory2, color blue
  - theory0 theory size 4
  - theory1 theory size 4
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere yes
  - item: i5, green, cube yes
  - item: i2, green, cube no
  - item: i0, green, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory3, shape cube
  - predict yes if: theory1, ранг blue
  - predict yes if: theory1, ранг blue
  - theory2 2 theory size
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, ранг red
  - predict yes if: theory0, ранг къацу
  - theory3 4 theory size
  - predict yes if: theory2, ранг blue
  - theory0 4 theory size
  - theory1 4 theory size
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, blue, sphere yes
  - item: i5, къацу, cube yes
  - item: i2, къацу, cube no
  - item: i0, къацу, cube yes
  - item: i6, red, cone yes
  - item: i3, red, sphere yes
  - item: i7, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - theory2 theory size 2
  - predicts yes if: theory1, colour and green
  - theory3 theory size 2
  - theory1 theory size 2
  - predicts yes if: theory0, colour and red
  - theory0 theory size 2
  - predicts yes if: theory2, colour and green
  - predicts yes if: theory3, shape and sphere
Costs: exception cost 4.
The data are:
  - item: i1, blue, sphere and no
  - item: i4, green, sphere and no
  - item: i6, red, cube and yes
  - item: i0, blue, cone and yes
  - item: i7, blue, sphere and no
  - item: i2, green, cone and no
  - item: i5, green, cone and yes
  - item: i3, red, cube and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - theory2 el tamaño de la teoría 2
  - predice sí si: theory1, es de color y verde
  - theory3 el tamaño de la teoría 2
  - theory1 el tamaño de la teoría 2
  - predice sí si: theory0, es de color y rojo
  - theory0 el tamaño de la teoría 2
  - predice sí si: theory2, es de color y verde
  - predice sí si: theory3, forma y esfera
Costes: exception el coste: 4.
Los datos son:
  - el elemento: i1, azul, esfera y no
  - el elemento: i4, verde, esfera y no
  - el elemento: i6, rojo, cubo y sí
  - el elemento: i0, azul, cono y sí
  - el elemento: i7, azul, esfera y no
  - el elemento: i2, verde, cono y no
  - el elemento: i5, verde, cono y sí
  - el elemento: i3, rojo, cubo y no
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - theory2的理论规模是2
  - 预测是若：theory1、颜色和绿色的
  - theory3的理论规模是2
  - theory1的理论规模是2
  - 预测是若：theory0、颜色和红色的
  - theory0的理论规模是2
  - 预测是若：theory2、颜色和绿色的
  - 预测是若：theory3、形状和球体
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i1、蓝色的、球体和否
  - 的物品是：i4、绿色的、球体和否
  - 的物品是：i6、红色的、立方体和是
  - 的物品是：i0、蓝色的、圆锥和是
  - 的物品是：i7、蓝色的、球体和否
  - 的物品是：i2、绿色的、圆锥和否
  - 的物品是：i5、绿色的、圆锥和是
  - 的物品是：i3、红色的、立方体和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - theory2 2 kuram boyut
  - öngörür evet eğer: theory1, renk ve yeşil
  - theory3 2 kuram boyut
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory0, renk ve kırmızı
  - theory0 2 kuram boyut
  - öngörür evet eğer: theory2, renk ve yeşil
  - öngörür evet eğer: theory3, şekil ve küre
Cost model: exception maliyet: 4.
Veriler:
  - item: i1, mavi, küre ve hayır
  - item: i4, yeşil, küre ve hayır
  - item: i6, kırmızı, küp ve evet
  - item: i0, mavi, koni ve evet
  - item: i7, mavi, küre ve hayır
  - item: i2, yeşil, koni ve hayır
  - item: i5, yeşil, koni ve evet
  - item: i3, kırmızı, küp ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - theory2 theory size 2
  - predict ndiyo kama: theory1, rangi na kijani
  - theory3 theory size 2
  - theory1 theory size 2
  - predict ndiyo kama: theory0, rangi na ekundu
  - theory0 theory size 2
  - predict ndiyo kama: theory2, rangi na kijani
  - predict ndiyo kama: theory3, umbo na tufe
Cost model: exception gharimu: 4.
Data:
  - item: i1, buluu, tufe na hapana
  - item: i4, kijani, tufe na hapana
  - item: i6, ekundu, mchemraba na ndiyo
  - item: i0, buluu, koni na ndiyo
  - item: i7, buluu, tufe na hapana
  - item: i2, kijani, koni na hapana
  - item: i5, kijani, koni na ndiyo
  - item: i3, ekundu, mchemraba na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - theory2 theory size 2
  - predicts yes if: theory1, colour and green
  - theory3 theory size 2
  - theory1 theory size 2
  - predicts yes if: theory0, colour and red
  - theory0 theory size 2
  - predicts yes if: theory2, colour and green
  - predicts yes if: theory3, shape and sphere
Costs: exception cost 4.
The data are:
  - item: i1, blue, sphere and no
  - item: i4, green, sphere and no
  - item: i6, red, cube and yes
  - item: i0, blue, cone and yes
  - item: i7, blue, sphere and no
  - item: i2, green, cone and no
  - item: i5, green, cone and yes
  - item: i3, red, cube and no
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(theory_size theory2 2) (predicts_yes_if theory1 color green) (theory_size theory3 2) (theory_size theory1 2) (predicts_yes_if theory0 color red) (theory_size theory0 2) (predicts_yes_if theory2 color green) (predicts_yes_if theory3 shape sphere)], cost_model: [(exception_cost 4)], data: [(item i1 blue sphere no) (item i4 green sphere no) (item i6 red cube yes) (item i0 blue cone yes) (item i7 blue sphere no) (item i2 green cone no) (item i5 green cone yes) (item i3 red cube no)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - theory2 Theorie Größe 2
  - vorhersagen ja wenn: theory1, Farbe und grün
  - theory3 Theorie Größe 2
  - theory1 Theorie Größe 2
  - vorhersagen ja wenn: theory0, Farbe und rot
  - theory0 Theorie Größe 2
  - vorhersagen ja wenn: theory2, Farbe und grün
  - vorhersagen ja wenn: theory3, Form und Kugel
Kosten: Ausnahme kosten 4.
Die Daten sind:
  - Ding: i1, blau, Kugel und nein
  - Ding: i4, grün, Kugel und nein
  - Ding: i6, rot, Kubus und ja
  - Ding: i0, blau, Kegel und ja
  - Ding: i7, blau, Kugel und nein
  - Ding: i2, grün, Kegel und nein
  - Ding: i5, grün, Kegel und ja
  - Ding: i3, rot, Kubus und nein
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - theory2 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory1, бо̀ја и зѐлен
  - theory3 тѐо̄рија величѝна 2
  - theory1 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory0, бо̀ја и цр̀вен
  - theory0 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory2, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory3, о̏блӣк и сфе́ра
Trošak мо̀дел: изузе́так ко̏штати 4.
Податци:
  - item: i1, пла̑в, сфе́ра и no
  - item: i4, зѐлен, сфе́ра и no
  - item: i6, цр̀вен, коцка и да
  - item: i0, пла̑в, stožac и да
  - item: i7, пла̑в, сфе́ра и no
  - item: i2, зѐлен, stožac и no
  - item: i5, зѐлен, stožac и да
  - item: i3, цр̀вен, коцка и no
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - theory2 teoria koko 2
  - ennakoida kyllä jos: theory1, väri ja vihreä
  - theory3 teoria koko 2
  - theory1 teoria koko 2
  - ennakoida kyllä jos: theory0, väri ja punainen
  - theory0 teoria koko 2
  - ennakoida kyllä jos: theory2, väri ja vihreä
  - ennakoida kyllä jos: theory3, muoto ja pallo
Kustannus malli: poikkeus maksaa 4.
Tieto:
  - esine: i1, sininen, pallo ja ei
  - esine: i4, vihreä, pallo ja ei
  - esine: i6, punainen, kuutio ja kyllä
  - esine: i0, sininen, kartio ja kyllä
  - esine: i7, sininen, pallo ja ei
  - esine: i2, vihreä, kartio ja ei
  - esine: i5, vihreä, kartio ja kyllä
  - esine: i3, punainen, kuutio ja ei
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - theory2的理论规模是2
  - 预测是若：theory1、颜色和绿色的
  - theory3的理论规模是2
  - theory1的理论规模是2
  - 预测是若：theory0、颜色和红色的
  - theory0的理论规模是2
  - 预测是若：theory2、颜色和绿色的
  - 预测是若：theory3、形状和球体
代价模型如下：exception的成本：4。
数据如下：
  - 的物品是：i1、蓝色的、球体和否
  - 的物品是：i4、绿色的、球体和否
  - 的物品是：i6、红色的、立方体和是
  - 的物品是：i0、蓝色的、圆锥和是
  - 的物品是：i7、蓝色的、球体和否
  - 的物品是：i2、绿色的、圆锥和否
  - 的物品是：i5、绿色的、圆锥和是
  - 的物品是：i3、红色的、立方体和否
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - theory22理論大きさ
  - 予言するはいもし...:theory1, 色と緑の
  - theory32理論大きさ
  - theory12理論大きさ
  - 予言するはいもし...:theory0, 色と赤い
  - theory02理論大きさ
  - 予言するはいもし...:theory2, 色と緑の
  - 予言するはいもし...:theory3, 形と球
費用モデル:例外費やす:4.
資料:
  - 項目:i1, 青い, 球といいえ
  - 項目:i4, 緑の, 球といいえ
  - 項目:i6, 赤い, 立方体とはい
  - 項目:i0, 青い, 松毬とはい
  - 項目:i7, 青い, 球といいえ
  - 項目:i2, 緑の, 松毬といいえ
  - 項目:i5, 緑の, 松毬とはい
  - 項目:i3, 赤い, 立方体といいえ
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - theory2 2 kuram boyut
  - öngörür evet eğer: theory1, renk ve yeşil
  - theory3 2 kuram boyut
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory0, renk ve kırmızı
  - theory0 2 kuram boyut
  - öngörür evet eğer: theory2, renk ve yeşil
  - öngörür evet eğer: theory3, şekil ve küre
Cost model: exception maliyet: 4.
Veriler:
  - item: i1, mavi, küre ve hayır
  - item: i4, yeşil, küre ve hayır
  - item: i6, kırmızı, küp ve evet
  - item: i0, mavi, koni ve evet
  - item: i7, mavi, küre ve hayır
  - item: i2, yeşil, koni ve hayır
  - item: i5, yeşil, koni ve evet
  - item: i3, kırmızı, küp ve hayır
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - نَظَرِيَّة مَقَاس theory2 2
  - تَنَبَّأَ نَعَم إِنْ: theory1، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory3 2
  - نَظَرِيَّة مَقَاس theory1 2
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَحْمَر
  - نَظَرِيَّة مَقَاس theory0 2
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كُرَة
cost model: اِسْتِثْنَاء cost 4.
بَيَانَات:
  - بَنْد: i1، أَزْرَق، كُرَة وَـ no
  - بَنْد: i4، أَخْضَر، كُرَة وَـ no
  - بَنْد: i6، أَحْمَر، كَعْبَة وَـ نَعَم
  - بَنْد: i0، أَزْرَق، مَخْرُوط وَـ نَعَم
  - بَنْد: i7، أَزْرَق، كُرَة وَـ no
  - بَنْد: i2، أَخْضَر، مَخْرُوط وَـ no
  - بَنْد: i5، أَخْضَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i3، أَحْمَر، كَعْبَة وَـ no
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - theory2 2 თეორია ზომა
  - predict დიახ თუ: theory1, ფერი და მწვანე
  - theory3 2 თეორია ზომა
  - theory1 2 თეორია ზომა
  - predict დიახ თუ: theory0, ფერი და წითელი
  - theory0 2 თეორია ზომა
  - predict დიახ თუ: theory2, ფერი და მწვანე
  - predict დიახ თუ: theory3, ფორმა და სფერო
Ფასი მოდელი: გამონაკლისი ღირს 4.
Მონაცემები:
  - item: i1, ლურჯი, სფერო და no
  - item: i4, მწვანე, სფერო და no
  - item: i6, წითელი, კუბი და დიახ
  - item: i0, ლურჯი, გირჩა და დიახ
  - item: i7, ლურჯი, სფერო და no
  - item: i2, მწვანე, გირჩა და no
  - item: i5, მწვანე, გირჩა და დიახ
  - item: i3, წითელი, კუბი და no
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - ariā size theory2 2
  - matakite āe mēnā: theory1, kano me kākāriki
  - ariā size theory3 2
  - ariā size theory1 2
  - matakite āe mēnā: theory0, kano me red
  - ariā size theory0 2
  - matakite āe mēnā: theory2, kano me kākāriki
  - matakite āe mēnā: theory3, āhuahanga me poi
Cost model: hāungatanga cost 4.
Hōtuku:
  - tūemi: i1, kahurangi, poi me kāo
  - tūemi: i4, kākāriki, poi me kāo
  - tūemi: i6, red, mataono me āe
  - tūemi: i0, kahurangi, koeko me āe
  - tūemi: i7, kahurangi, poi me kāo
  - tūemi: i2, kākāriki, koeko me kāo
  - tūemi: i5, kākāriki, koeko me āe
  - tūemi: i3, red, mataono me kāo
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - theory2 lý thuyết kích thước 2
  - dự báo vâng nếu: theory1, màu và green
  - theory3 lý thuyết kích thước 2
  - theory1 lý thuyết kích thước 2
  - dự báo vâng nếu: theory0, màu và đỏ
  - theory0 lý thuyết kích thước 2
  - dự báo vâng nếu: theory2, màu và green
  - dự báo vâng nếu: theory3, hình dáng và mặt cầu
Cost model: lệ ngoại cost 4.
Dữ liệu:
  - vật phẩm: i1, blue, mặt cầu và no
  - vật phẩm: i4, green, mặt cầu và no
  - vật phẩm: i6, đỏ, lập phương và vâng
  - vật phẩm: i0, blue, nón và vâng
  - vật phẩm: i7, blue, mặt cầu và no
  - vật phẩm: i2, green, nón và no
  - vật phẩm: i5, green, nón và vâng
  - vật phẩm: i3, đỏ, lập phương và no
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - theory2ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory1, สีและเขียว
  - theory3ทฤษฎีขนาด2
  - theory1ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory0, สีและแดง
  - theory0ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory2, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและทรงกลม
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:4.
ข้อมูล:
  - item:i1, น้ำเงิน, ทรงกลมและไฮ้
  - item:i4, เขียว, ทรงกลมและไฮ้
  - item:i6, แดง, ลูกบาศก์และใช่
  - item:i0, น้ำเงิน, กรวยและใช่
  - item:i7, น้ำเงิน, ทรงกลมและไฮ้
  - item:i2, เขียว, กรวยและไฮ้
  - item:i5, เขียว, กรวยและใช่
  - item:i3, แดง, ลูกบาศก์และไฮ้
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - theory2 2 theory size
  - கணி ஆம் என்றால்: theory1, நிறம் மற்றும் பச்சை
  - theory3 2 theory size
  - theory1 2 theory size
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் சிவப்பு
  - theory0 2 theory size
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory3, shape மற்றும் sphere
cost model: exception செலவு 4.
தரவுகள்:
  - item: i1, நீலம், sphere மற்றும் no
  - item: i4, பச்சை, sphere மற்றும் no
  - item: i6, சிவப்பு, cube மற்றும் ஆம்
  - item: i0, நீலம், cone மற்றும் ஆம்
  - item: i7, நீலம், sphere மற்றும் no
  - item: i2, பச்சை, cone மற்றும் no
  - item: i5, பச்சை, cone மற்றும் ஆம்
  - item: i3, சிவப்பு, cube மற்றும் no
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - theory2 theory size 2
  - predict ndiyo kama: theory1, rangi na kijani
  - theory3 theory size 2
  - theory1 theory size 2
  - predict ndiyo kama: theory0, rangi na ekundu
  - theory0 theory size 2
  - predict ndiyo kama: theory2, rangi na kijani
  - predict ndiyo kama: theory3, umbo na tufe
Cost model: exception gharimu: 4.
Data:
  - item: i1, buluu, tufe na hapana
  - item: i4, kijani, tufe na hapana
  - item: i6, ekundu, mchemraba na ndiyo
  - item: i0, buluu, koni na ndiyo
  - item: i7, buluu, tufe na hapana
  - item: i2, kijani, koni na hapana
  - item: i5, kijani, koni na ndiyo
  - item: i3, ekundu, mchemraba na hapana
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - theory2 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory1, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory3 2 theory size
  - theory1 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó łichííʼ
  - theory0 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó sphere
Cost model: exception bą́ą́h ílį́ 4.
Data:
  - item: i1, dootłʼizh, sphere dóó no
  - item: i4, dinootłʼizh, sphere dóó no
  - item: i6, łichííʼ, cube dóó aooʼ
  - item: i0, dootłʼizh, cone dóó aooʼ
  - item: i7, dootłʼizh, sphere dóó no
  - item: i2, dinootłʼizh, cone dóó no
  - item: i5, dinootłʼizh, cone dóó aooʼ
  - item: i3, łichííʼ, cube dóó no
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - theory2 2 theory size
  - predict aap if: theory1, qalipaat aamma qorsuk
  - theory3 2 theory size
  - theory1 2 theory size
  - predict aap if: theory0, qalipaat aamma aappalajaarpoq
  - theory0 2 theory size
  - predict aap if: theory2, qalipaat aamma qorsuk
  - predict aap if: theory3, shape aamma sphere
Cost model: exception cost 4.
Data:
  - item: i1, tungujortoq, sphere aamma no
  - item: i4, qorsuk, sphere aamma no
  - item: i6, aappalajaarpoq, cube aamma aap
  - item: i0, tungujortoq, cone aamma aap
  - item: i7, tungujortoq, sphere aamma no
  - item: i2, qorsuk, cone aamma no
  - item: i5, qorsuk, cone aamma aap
  - item: i3, aappalajaarpoq, cube aamma no
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict ya sapos: theory1, kala na grinpela
  - theory3 theory size 2
  - theory1 theory size 2
  - predict ya sapos: theory0, kala na ret
  - theory0 theory size 2
  - predict ya sapos: theory2, kala na grinpela
  - predict ya sapos: theory3, shape na sphere
Cost model: exception cost 4.
Data:
  - item: i1, blu, sphere na no
  - item: i4, grinpela, sphere na no
  - item: i6, ret, cube na ya
  - item: i0, blu, cone na ya
  - item: i7, blu, sphere na no
  - item: i2, grinpela, cone na no
  - item: i5, grinpela, cone na ya
  - item: i3, ret, cube na no
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, color màxke
  - theory0 theory size 2
  - predict yes if: theory2, color green
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, màxke, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone yes
  - item: i3, màxke, cube no
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, бос а баьццара
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, бос а цӏе
  - theory0 theory size 2
  - predict yes if: theory2, бос а баьццара
  - predict yes if: theory3, shape а sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere а no
  - item: i4, баьццара, sphere а no
  - item: i6, цӏе, cube а yes
  - item: i0, blue, cone а yes
  - item: i7, blue, sphere а no
  - item: i2, баьццара, cone а no
  - item: i5, баьццара, cone а yes
  - item: i3, цӏе, cube а no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - theory2 2 이론 크기
  - 예언하다 네 만약: theory1, 색깔 녹색의
  - theory3 2 이론 크기
  - theory1 2 이론 크기
  - 예언하다 네 만약: theory0, 색깔 빨갛다
  - theory0 2 이론 크기
  - 예언하다 네 만약: theory2, 색깔 녹색의
  - 예언하다 네 만약: theory3, 모양 구체
cost model: 예외 cost 4.
자료:
  - 아이템: i1, 푸르다, 구체 아니요
  - 아이템: i4, 녹색의, 구체 아니요
  - 아이템: i6, 빨갛다, 정육면체 네
  - 아이템: i0, 푸르다, 솔방울 네
  - 아이템: i7, 푸르다, 구체 아니요
  - 아이템: i2, 녹색의, 솔방울 아니요
  - 아이템: i5, 녹색의, 솔방울 네
  - 아이템: i3, 빨갛다, 정육면체 아니요
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - theory2 2 theory tamaina
  - aurresan bai if: theory1, kolore eta berde
  - theory3 2 theory tamaina
  - theory1 2 theory tamaina
  - aurresan bai if: theory0, kolore eta gorri
  - theory0 2 theory tamaina
  - aurresan bai if: theory2, kolore eta berde
  - aurresan bai if: theory3, shape eta esfera
Cost model: exception kostatu 4.
Data:
  - item: i1, urdin, esfera eta no
  - item: i4, berde, esfera eta no
  - item: i6, gorri, kubo eta bai
  - item: i0, urdin, pinaburu eta bai
  - item: i7, urdin, esfera eta no
  - item: i2, berde, pinaburu eta no
  - item: i5, berde, pinaburu eta bai
  - item: i3, gorri, kubo eta no
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - theory2 theory size 2
  - predict ᎥᎥ if: theory1, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory3 theory size 2
  - theory1 theory size 2
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - theory0 theory size 2
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory3, shape ᎠᎴ sphere
Cost model: exception cost 4.
Data:
  - item: i1, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i4, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᏝᏃ
  - item: i6, ᎩᎦᎨ, cube ᎠᎴ ᎥᎥ
  - item: i0, ᏌᎪᏂᎨᎢ, cone ᎠᎴ ᎥᎥ
  - item: i7, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i2, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᏝᏃ
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᎥᎥ
  - item: i3, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory2 theory chhika 2
  - predict arí if: theory1, llimphi waylla
  - theory3 theory chhika 2
  - theory1 theory chhika 2
  - predict arí if: theory0, llimphi puka
  - theory0 theory chhika 2
  - predict arí if: theory2, llimphi waylla
  - predict arí if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, anqas, sphere mánan
  - item: i4, waylla, sphere mánan
  - item: i6, puka, cube arí
  - item: i0, anqas, cone arí
  - item: i7, anqas, sphere mánan
  - item: i2, waylla, cone mánan
  - item: i5, waylla, cone arí
  - item: i3, puka, cube mánan
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 тиориэ size
  - predict yes if: theory1, color ыкӏи уцышъо
  - theory3 2 тиориэ size
  - theory1 2 тиориэ size
  - predict yes if: theory0, color ыкӏи плъыжьы
  - theory0 2 тиориэ size
  - predict yes if: theory2, color ыкӏи уцышъо
  - predict yes if: theory3, shape ыкӏи sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere ыкӏи no
  - item: i4, уцышъо, sphere ыкӏи no
  - item: i6, плъыжьы, cube ыкӏи yes
  - item: i0, blue, cone ыкӏи yes
  - item: i7, blue, sphere ыкӏи no
  - item: i2, уцышъо, cone ыкӏи no
  - item: i5, уцышъо, cone ыкӏи yes
  - item: i3, плъыжьы, cube ыкӏи no
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, xim thiab ntsuab
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, xim thiab liab
  - theory0 theory size 2
  - predict yes if: theory2, xim thiab ntsuab
  - predict yes if: theory3, shape thiab sphere
Cost model: exception nqi 4.
Data:
  - item: i1, xiav, sphere thiab no
  - item: i4, ntsuab, sphere thiab no
  - item: i6, liab, cube thiab yes
  - item: i0, xiav, cone thiab yes
  - item: i7, xiav, sphere thiab no
  - item: i2, ntsuab, cone thiab no
  - item: i5, ntsuab, cone thiab yes
  - item: i3, liab, cube thiab no
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict ngai if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict ngai if: theory0, color red
  - theory0 theory size 2
  - predict ngai if: theory2, color green
  - predict ngai if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere woi
  - item: i4, green, sphere woi
  - item: i6, red, cube ngai
  - item: i0, blue, cone ngai
  - item: i7, blue, sphere woi
  - item: i2, green, cone woi
  - item: i5, green, cone ngai
  - item: i3, red, cube woi
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - theory2 theory size 2
  - predict i̠i if: theory1, color micha' green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict i̠i if: theory0, color micha' homa
  - theory0 theory size 2
  - predict i̠i if: theory2, color micha' green
  - predict i̠i if: theory3, shape micha' sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere micha' no
  - item: i4, green, sphere micha' no
  - item: i6, homa, cube micha' i̠i
  - item: i0, blue, cone micha' i̠i
  - item: i7, blue, sphere micha' no
  - item: i2, green, cone micha' no
  - item: i5, green, cone micha' i̠i
  - item: i3, homa, cube micha' no
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory1, color カ green
  - theory3 2 theory size
  - theory1 2 theory size
  - predict yes if: theory0, color カ フレ
  - theory0 2 theory size
  - predict yes if: theory2, color カ green
  - predict yes if: theory3, shape カ sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere カ ソモ
  - item: i4, green, sphere カ ソモ
  - item: i6, フレ, cube カ yes
  - item: i0, blue, cone カ yes
  - item: i7, blue, sphere カ ソモ
  - item: i2, green, cone カ ソモ
  - item: i5, green, cone カ yes
  - item: i3, フレ, cube カ ソモ
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes эвыр: theory1, пэран ынкъам green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes эвыр: theory0, пэран ынкъам чеԓгыԓьын
  - theory0 theory size 2
  - predict yes эвыр: theory2, пэран ынкъам green
  - predict yes эвыр: theory3, shape ынкъам sphere
Cost model: exception cost 4.
Data:
  - item: i1, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i4, green, sphere ынкъам no
  - item: i6, чеԓгыԓьын, cube ынкъам yes
  - item: i0, ныԓиԓиԓӄин, cone ынкъам yes
  - item: i7, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i2, green, cone ынкъам no
  - item: i5, green, cone ынкъам yes
  - item: i3, чеԓгыԓьын, cube ынкъам no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes wa: theory1, color yéetel yaʼax
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes wa: theory0, color yéetel chak
  - theory0 theory size 2
  - predict yes wa: theory2, color yéetel yaʼax
  - predict yes wa: theory3, shape yéetel sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere yéetel no
  - item: i4, yaʼax, sphere yéetel no
  - item: i6, chak, cube yéetel yes
  - item: i0, blue, cone yéetel yes
  - item: i7, blue, sphere yéetel no
  - item: i2, yaʼax, cone yéetel no
  - item: i5, yaʼax, cone yéetel yes
  - item: i3, chak, cube yéetel no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict hą if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict hą if: theory0, color red
  - theory0 theory size 2
  - predict hą if: theory2, color green
  - predict hą if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, red, cube hą
  - item: i0, blue, cone hą
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone hą
  - item: i3, red, cube no
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory1, color na green
  - theory3 2 theory size
  - theory1 2 theory size
  - predict yes if: theory0, color na red
  - theory0 2 theory size
  - predict yes if: theory2, color na green
  - predict yes if: theory3, shape na sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere na no
  - item: i4, green, sphere na no
  - item: i6, red, cube na yes
  - item: i0, blue, cone na yes
  - item: i7, blue, sphere na no
  - item: i2, green, cone na no
  - item: i5, green, cone na yes
  - item: i3, red, cube na no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict heẽ if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict heẽ if: theory0, color pytã
  - theory0 theory size 2
  - predict heẽ if: theory2, color green
  - predict heẽ if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere nahániri
  - item: i4, green, sphere nahániri
  - item: i6, pytã, cube heẽ
  - item: i0, blue, cone heẽ
  - item: i7, blue, sphere nahániri
  - item: i2, green, cone nahániri
  - item: i5, green, cone heẽ
  - item: i3, pytã, cube nahániri
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict jisa if: theory1, sami ch’uxña
  - theory3 2 theory size
  - theory1 2 theory size
  - predict jisa if: theory0, sami chupika
  - theory0 2 theory size
  - predict jisa if: theory2, sami ch’uxña
  - predict jisa if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, larama, sphere no
  - item: i4, ch’uxña, sphere no
  - item: i6, chupika, cube jisa
  - item: i0, larama, cone jisa
  - item: i7, larama, sphere no
  - item: i2, ch’uxña, cone no
  - item: i5, ch’uxña, cone jisa
  - item: i3, chupika, cube no
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory ʻǁnàhã 2
  - predict ʻée if: theory1, color ǂʻá green
  - theory3 theory ʻǁnàhã 2
  - theory1 theory ʻǁnàhã 2
  - predict ʻée if: theory0, color ǂʻá red
  - theory0 theory ʻǁnàhã 2
  - predict ʻée if: theory2, color ǂʻá green
  - predict ʻée if: theory3, shape ǂʻá sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere ǂʻá ʻĩĩ
  - item: i4, green, sphere ǂʻá ʻĩĩ
  - item: i6, red, cube ǂʻá ʻée
  - item: i0, blue, cone ǂʻá ʻée
  - item: i7, blue, sphere ǂʻá ʻĩĩ
  - item: i2, green, cone ǂʻá ʻĩĩ
  - item: i5, green, cone ǂʻá ʻée
  - item: i3, red, cube ǂʻá ʻĩĩ
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory1, вал green
  - theory3 2 theory size
  - theory1 2 theory size
  - predict yes if: theory0, вал red
  - theory0 2 theory size
  - predict yes if: theory2, вал green
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, red, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone yes
  - item: i3, red, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, color red
  - theory0 theory size 2
  - predict yes if: theory2, color green
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, red, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone yes
  - item: i3, red, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory1, color green
  - theory3 2 theory size
  - theory1 2 theory size
  - predict yes if: theory0, color red
  - theory0 2 theory size
  - predict yes if: theory2, color green
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, red, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone yes
  - item: i3, red, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict ee if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict ee if: theory0, color red
  - theory0 theory size 2
  - predict ee if: theory2, color green
  - predict ee if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, red, cube ee
  - item: i0, blue, cone ee
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone ee
  - item: i3, red, cube no
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, color red
  - theory0 theory size 2
  - predict yes if: theory2, color green
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere inte
  - item: i4, green, sphere inte
  - item: i6, red, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere inte
  - item: i2, green, cone inte
  - item: i5, green, cone yes
  - item: i3, red, cube inte
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - theory2 théorie taille 2
  - prédire oui si: theory1, couleur et vert
  - theory3 théorie taille 2
  - theory1 théorie taille 2
  - prédire oui si: theory0, couleur et rouge
  - theory0 théorie taille 2
  - prédire oui si: theory2, couleur et vert
  - prédire oui si: theory3, forme et sphère
Coûts : exception coûter 4.
Les données sont:
  - article: i1, bleu, sphère et no
  - article: i4, vert, sphère et no
  - article: i6, rouge, cube et oui
  - article: i0, bleu, cône et oui
  - article: i7, bleu, sphère et no
  - article: i2, vert, cône et no
  - article: i5, vert, cône et oui
  - article: i3, rouge, cube et no
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - theory2 teorio grandeco 2
  - predict jes se: theory1, koloro kaj verda
  - theory3 teorio grandeco 2
  - theory1 teorio grandeco 2
  - predict jes se: theory0, koloro kaj ruĝa
  - theory0 teorio grandeco 2
  - predict jes se: theory2, koloro kaj verda
  - predict jes se: theory3, formi kaj sfero
Kosto pozistino: escepto kosti 4.
Datumo:
  - ero: i1, blua, sfero kaj no
  - ero: i4, verda, sfero kaj no
  - ero: i6, ruĝa, kubo kaj jes
  - ero: i0, blua, strobilo kaj jes
  - ero: i7, blua, sfero kaj no
  - ero: i2, verda, strobilo kaj no
  - ero: i5, verda, strobilo kaj jes
  - ero: i3, ruĝa, kubo kaj no
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - theory2 elmélet méret 2
  - megjósol igen ha: theory1, szín és zöld
  - theory3 elmélet méret 2
  - theory1 elmélet méret 2
  - megjósol igen ha: theory0, szín és piros
  - theory0 elmélet méret 2
  - megjósol igen ha: theory2, szín és zöld
  - megjósol igen ha: theory3, alak és gömb
Költség manöken: kivétel kerül 4.
Adat:
  - cikk: i1, kék, gömb és no
  - cikk: i4, zöld, gömb és no
  - cikk: i6, piros, kocka és igen
  - cikk: i0, kék, toboz és igen
  - cikk: i7, kék, gömb és no
  - cikk: i2, zöld, toboz és no
  - cikk: i5, zöld, toboz és igen
  - cikk: i3, piros, kocka és no
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - theory2theory size2
  - predict係要是:theory1, 色及綠色嘅
  - theory3theory size2
  - theory1theory size2
  - predict係要是:theory0, 色及紅
  - theory0theory size2
  - predict係要是:theory2, 色及綠色嘅
  - predict係要是:theory3, 形狀及sphere
cost model:exception cost:4.
數據:
  - item:i1, 藍色嘅, sphere及no
  - item:i4, 綠色嘅, sphere及no
  - item:i6, 紅, cube及係
  - item:i0, 藍色嘅, cone及係
  - item:i7, 藍色嘅, sphere及no
  - item:i2, 綠色嘅, cone及no
  - item:i5, 綠色嘅, cone及係
  - item:i3, 紅, cube及no
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, color とぅ green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, color とぅ red
  - theory0 theory size 2
  - predict yes if: theory2, color とぅ green
  - predict yes if: theory3, shape とぅ sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere とぅ no
  - item: i4, green, sphere とぅ no
  - item: i6, red, cube とぅ yes
  - item: i0, blue, cone とぅ yes
  - item: i7, blue, sphere とぅ no
  - item: i2, green, cone とぅ no
  - item: i5, green, cone とぅ yes
  - item: i3, red, cube とぅ no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - theory2 теория мөлшер 2
  - predict иә егер: theory1, түс және жасыл
  - theory3 теория мөлшер 2
  - theory1 теория мөлшер 2
  - predict иә егер: theory0, түс және қызыл
  - theory0 теория мөлшер 2
  - predict иә егер: theory2, түс және жасыл
  - predict иә егер: theory3, нысан және сфера
Тұру үлгі: ерекшелік тұру 4.
Деректер:
  - зат: i1, көк, сфера және жоқ
  - зат: i4, жасыл, сфера және жоқ
  - зат: i6, қызыл, куб және иә
  - зат: i0, көк, бүршік және иә
  - зат: i7, көк, сфера және жоқ
  - зат: i2, жасыл, бүршік және жоқ
  - зат: i5, жасыл, бүршік және иә
  - зат: i3, қызыл, куб және жоқ
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - theory2 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory1, צֶבַע וְ־ ירוק מקנאה
  - theory3 תּוֹרָה גֹּדֶל 2
  - theory1 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory0, צֶבַע וְ־ אָדוֹם
  - theory0 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory2, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory3, צוּרָה וְ־ כַּדּוּר
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 4.
נְתוּנִים:
  - אבזר: i1, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i4, ירוק מקנאה, כַּדּוּר וְ־ no
  - אבזר: i6, אָדוֹם, קובייה וְ־ כֵּן
  - אבזר: i0, כָּחוֹל, אצטרובל וְ־ כֵּן
  - אבזר: i7, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i2, ירוק מקנאה, אצטרובל וְ־ no
  - אבזר: i5, ירוק מקנאה, אצטרובל וְ־ כֵּן
  - אבזר: i3, אָדוֹם, קובייה וְ־ no
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - theory2 theory size 2
  - predict იახ if: theory1, color დო green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict იახ if: theory0, color დო red
  - theory0 theory size 2
  - predict იახ if: theory2, color დო green
  - predict იახ if: theory3, shape დო sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere დო no
  - item: i4, green, sphere დო no
  - item: i6, red, cube დო იახ
  - item: i0, blue, cone დო იახ
  - item: i7, blue, sphere დო no
  - item: i2, green, cone დო no
  - item: i5, green, cone დო იახ
  - item: i3, red, cube დო no
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - theory2 teori ukuran 2
  - predict ya jika: theory1, warna dan hijau
  - theory3 teori ukuran 2
  - theory1 teori ukuran 2
  - predict ya jika: theory0, warna dan merah
  - theory0 teori ukuran 2
  - predict ya jika: theory2, warna dan hijau
  - predict ya jika: theory3, bentuk dan bola
Cost model: pengecualian menghabiskan biaya 4.
Data:
  - benda: i1, biru, bola dan no
  - benda: i4, hijau, bola dan no
  - benda: i6, merah, kubus dan ya
  - benda: i0, biru, kerucut dan ya
  - benda: i7, biru, bola dan no
  - benda: i2, hijau, kerucut dan no
  - benda: i5, hijau, kerucut dan ya
  - benda: i3, merah, kubus dan no
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - theory2ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory1, ពណ៌នឹងបៃតង
  - theory3ទ្រឹស្ដីទំហំ2
  - theory1ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងក្រហម
  - theory0ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory3, គ្រោងនឹងគោល
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:4.
ទិន្នន័យ:
  - item:i1, ខៀវ, គោលនឹងទេ
  - item:i4, បៃតង, គោលនឹងទេ
  - item:i6, ក្រហម, គូបនឹងច៎ា
  - item:i0, ខៀវ, កោណនឹងច៎ា
  - item:i7, ខៀវ, គោលនឹងទេ
  - item:i2, បៃតង, កោណនឹងទេ
  - item:i5, បៃតង, កោណនឹងច៎ា
  - item:i3, ក្រហម, គូបនឹងទេ
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - theory2ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ສີແລະຂຽວ
  - theory3ທິດສະດີຂະຫນາດ2
  - theory1ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະແດງ
  - theory0ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ຮູບຮ່າງແລະຄະດີ
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:4.
ຂໍ້ມູນ:
  - item:i1, ຟ້າ, ຄະດີແລະno
  - item:i4, ຂຽວ, ຄະດີແລະno
  - item:i6, ແດງ, ລູກບາດແລະແມ່ນ
  - item:i0, ຟ້າ, coneແລະແມ່ນ
  - item:i7, ຟ້າ, ຄະດີແລະno
  - item:i2, ຂຽວ, coneແລະno
  - item:i5, ຂຽວ, coneແລະແມ່ນ
  - item:i3, ແດງ, ລູກບາດແລະno
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - theory2 2 theory size
  - predict అవును అయితే: theory1, రంగు మరియు పచ్చని
  - theory3 2 theory size
  - theory1 2 theory size
  - predict అవును అయితే: theory0, రంగు మరియు ఎర్ర
  - theory0 2 theory size
  - predict అవును అయితే: theory2, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory3, ఆకారం మరియు గోళము
cost model: exception cost 4.
data:
  - item: i1, నీలము, గోళము మరియు no
  - item: i4, పచ్చని, గోళము మరియు no
  - item: i6, ఎర్ర, cube మరియు అవును
  - item: i0, నీలము, cone మరియు అవును
  - item: i7, నీలము, గోళము మరియు no
  - item: i2, పచ్చని, cone మరియు no
  - item: i5, పచ్చని, cone మరియు అవును
  - item: i3, ఎర్ర, cube మరియు no
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - theory2 theory size 2
  - predict hẹn if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict hẹn if: theory0, color pupa
  - theory0 theory size 2
  - predict hẹn if: theory2, color green
  - predict hẹn if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, dúdú, sphere rárá
  - item: i4, green, sphere rárá
  - item: i6, pupa, cube bẹ́ẹ̀ ni
  - item: i0, dúdú, cone bẹ́ẹ̀ ni
  - item: i7, dúdú, sphere rárá
  - item: i2, green, cone rárá
  - item: i5, green, cone bẹ́ẹ̀ ni
  - item: i3, pupa, cube rárá
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory2 2 theory size
  - predict au if: theory1, color green
  - theory3 2 theory size
  - theory1 2 theory size
  - predict au if: theory0, color łichíí
  - theory0 2 theory size
  - predict au if: theory2, color green
  - predict au if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, łichíí, cube au
  - item: i0, blue, cone au
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone au
  - item: i3, łichíí, cube no
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - theory2 theory size 2
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, color red
  - theory0 theory size 2
  - predict yes if: theory2, color green
  - predict yes if: theory3, shape sphere
cost model: exception cost 4.
data:
  - item: i1, blue, sphere ᐋᒃᑲ
  - item: i4, green, sphere ᐋᒃᑲ
  - item: i6, red, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere ᐋᒃᑲ
  - item: i2, green, cone ᐋᒃᑲ
  - item: i5, green, cone yes
  - item: i3, red, cube ᐋᒃᑲ
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, color grun
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, color red
  - theory0 theory size 2
  - predict yes if: theory2, color grun
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blaw, sphere no
  - item: i4, grun, sphere no
  - item: i6, red, cube yes
  - item: i0, blaw, cone yes
  - item: i7, blaw, sphere no
  - item: i2, grun, cone no
  - item: i5, grun, cone yes
  - item: i3, red, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - predict yes if: theory1, color green
  - theory3 theory size 2
  - theory1 theory size 2
  - predict yes if: theory0, color red
  - theory0 theory size 2
  - predict yes if: theory2, color green
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, green, sphere no
  - item: i6, red, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere no
  - item: i2, green, cone no
  - item: i5, green, cone yes
  - item: i3, red, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - predict yes if: theory1, ранг къацу
  - theory3 2 theory size
  - theory1 2 theory size
  - predict yes if: theory0, ранг red
  - theory0 2 theory size
  - predict yes if: theory2, ранг къацу
  - predict yes if: theory3, shape sphere
Cost model: exception cost 4.
Data:
  - item: i1, blue, sphere no
  - item: i4, къацу, sphere no
  - item: i6, red, cube yes
  - item: i0, blue, cone yes
  - item: i7, blue, sphere no
  - item: i2, къацу, cone no
  - item: i5, къацу, cone yes
  - item: i3, red, cube no
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - predicts yes if: theory1, shape and cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predicts yes if: theory3, colour and blue
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory0, colour and green
  - predicts yes if: theory2, colour and green
  - theory2 theory size 4
  - predicts yes if: theory2, shape and cube
Costs: exception cost 2.
The data are:
  - item: i3, red, sphere and no
  - item: i2, green, sphere and no
  - item: i5, blue, sphere and yes
  - item: i7, green, cone and no
  - item: i6, green, sphere and yes
  - item: i1, green, cone and yes
  - item: i4, red, sphere and yes
  - item: i0, blue, sphere and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - predice sí si: theory1, forma y cono
  - theory3 el tamaño de la teoría 2
  - theory0 el tamaño de la teoría 4
  - theory1 el tamaño de la teoría 2
  - predice sí si: theory3, es de color y azul
  - predice sí si: theory0, es de color y rojo
  - predice sí si: theory0, es de color y verde
  - predice sí si: theory2, es de color y verde
  - theory2 el tamaño de la teoría 4
  - predice sí si: theory2, forma y cubo
Costes: exception el coste: 2.
Los datos son:
  - el elemento: i3, rojo, esfera y no
  - el elemento: i2, verde, esfera y no
  - el elemento: i5, azul, esfera y sí
  - el elemento: i7, verde, cono y no
  - el elemento: i6, verde, esfera y sí
  - el elemento: i1, verde, cono y sí
  - el elemento: i4, rojo, esfera y sí
  - el elemento: i0, azul, esfera y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - 预测是若：theory1、形状和圆锥
  - theory3的理论规模是2
  - theory0的理论规模是4
  - theory1的理论规模是2
  - 预测是若：theory3、颜色和蓝色的
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory0、颜色和绿色的
  - 预测是若：theory2、颜色和绿色的
  - theory2的理论规模是4
  - 预测是若：theory2、形状和立方体
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i3、红色的、球体和否
  - 的物品是：i2、绿色的、球体和否
  - 的物品是：i5、蓝色的、球体和是
  - 的物品是：i7、绿色的、圆锥和否
  - 的物品是：i6、绿色的、球体和是
  - 的物品是：i1、绿色的、圆锥和是
  - 的物品是：i4、红色的、球体和是
  - 的物品是：i0、蓝色的、球体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - öngörür evet eğer: theory1, şekil ve koni
  - theory3 2 kuram boyut
  - theory0 4 kuram boyut
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory3, renk ve mavi
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory0, renk ve yeşil
  - öngörür evet eğer: theory2, renk ve yeşil
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory2, şekil ve küp
Cost model: exception maliyet: 2.
Veriler:
  - item: i3, kırmızı, küre ve hayır
  - item: i2, yeşil, küre ve hayır
  - item: i5, mavi, küre ve evet
  - item: i7, yeşil, koni ve hayır
  - item: i6, yeşil, küre ve evet
  - item: i1, yeşil, koni ve evet
  - item: i4, kırmızı, küre ve evet
  - item: i0, mavi, küre ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - predict ndiyo kama: theory1, umbo na koni
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict ndiyo kama: theory3, rangi na buluu
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory0, rangi na kijani
  - predict ndiyo kama: theory2, rangi na kijani
  - theory2 theory size 4
  - predict ndiyo kama: theory2, umbo na mchemraba
Cost model: exception gharimu: 2.
Data:
  - item: i3, ekundu, tufe na hapana
  - item: i2, kijani, tufe na hapana
  - item: i5, buluu, tufe na ndiyo
  - item: i7, kijani, koni na hapana
  - item: i6, kijani, tufe na ndiyo
  - item: i1, kijani, koni na ndiyo
  - item: i4, ekundu, tufe na ndiyo
  - item: i0, buluu, tufe na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - predicts yes if: theory1, shape and cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predicts yes if: theory3, colour and blue
  - predicts yes if: theory0, colour and red
  - predicts yes if: theory0, colour and green
  - predicts yes if: theory2, colour and green
  - theory2 theory size 4
  - predicts yes if: theory2, shape and cube
Costs: exception cost 2.
The data are:
  - item: i3, red, sphere and no
  - item: i2, green, sphere and no
  - item: i5, blue, sphere and yes
  - item: i7, green, cone and no
  - item: i6, green, sphere and yes
  - item: i1, green, cone and yes
  - item: i4, red, sphere and yes
  - item: i0, blue, sphere and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(predicts_yes_if theory1 shape cone) (theory_size theory3 2) (theory_size theory0 4) (theory_size theory1 2) (predicts_yes_if theory3 color blue) (predicts_yes_if theory0 color red) (predicts_yes_if theory0 color green) (predicts_yes_if theory2 color green) (theory_size theory2 4) (predicts_yes_if theory2 shape cube)], cost_model: [(exception_cost 2)], data: [(item i3 red sphere no) (item i2 green sphere no) (item i5 blue sphere yes) (item i7 green cone no) (item i6 green sphere yes) (item i1 green cone yes) (item i4 red sphere yes) (item i0 blue sphere yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - vorhersagen ja wenn: theory1, Form und Kegel
  - theory3 Theorie Größe 2
  - theory0 Theorie Größe 4
  - theory1 Theorie Größe 2
  - vorhersagen ja wenn: theory3, Farbe und blau
  - vorhersagen ja wenn: theory0, Farbe und rot
  - vorhersagen ja wenn: theory0, Farbe und grün
  - vorhersagen ja wenn: theory2, Farbe und grün
  - theory2 Theorie Größe 4
  - vorhersagen ja wenn: theory2, Form und Kubus
Kosten: Ausnahme kosten 2.
Die Daten sind:
  - Ding: i3, rot, Kugel und nein
  - Ding: i2, grün, Kugel und nein
  - Ding: i5, blau, Kugel und ja
  - Ding: i7, grün, Kegel und nein
  - Ding: i6, grün, Kugel und ja
  - Ding: i1, grün, Kegel und ja
  - Ding: i4, rot, Kugel und ja
  - Ding: i0, blau, Kugel und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - predvidjeti да ȁko: theory1, о̏блӣк и stožac
  - theory3 тѐо̄рија величѝна 2
  - theory0 тѐо̄рија величѝна 4
  - theory1 тѐо̄рија величѝна 2
  - predvidjeti да ȁko: theory3, бо̀ја и пла̑в
  - predvidjeti да ȁko: theory0, бо̀ја и цр̀вен
  - predvidjeti да ȁko: theory0, бо̀ја и зѐлен
  - predvidjeti да ȁko: theory2, бо̀ја и зѐлен
  - theory2 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory2, о̏блӣк и коцка
Trošak мо̀дел: изузе́так ко̏штати 2.
Податци:
  - item: i3, цр̀вен, сфе́ра и no
  - item: i2, зѐлен, сфе́ра и no
  - item: i5, пла̑в, сфе́ра и да
  - item: i7, зѐлен, stožac и no
  - item: i6, зѐлен, сфе́ра и да
  - item: i1, зѐлен, stožac и да
  - item: i4, цр̀вен, сфе́ра и да
  - item: i0, пла̑в, сфе́ра и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - ennakoida kyllä jos: theory1, muoto ja kartio
  - theory3 teoria koko 2
  - theory0 teoria koko 4
  - theory1 teoria koko 2
  - ennakoida kyllä jos: theory3, väri ja sininen
  - ennakoida kyllä jos: theory0, väri ja punainen
  - ennakoida kyllä jos: theory0, väri ja vihreä
  - ennakoida kyllä jos: theory2, väri ja vihreä
  - theory2 teoria koko 4
  - ennakoida kyllä jos: theory2, muoto ja kuutio
Kustannus malli: poikkeus maksaa 2.
Tieto:
  - esine: i3, punainen, pallo ja ei
  - esine: i2, vihreä, pallo ja ei
  - esine: i5, sininen, pallo ja kyllä
  - esine: i7, vihreä, kartio ja ei
  - esine: i6, vihreä, pallo ja kyllä
  - esine: i1, vihreä, kartio ja kyllä
  - esine: i4, punainen, pallo ja kyllä
  - esine: i0, sininen, pallo ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - 预测是若：theory1、形状和圆锥
  - theory3的理论规模是2
  - theory0的理论规模是4
  - theory1的理论规模是2
  - 预测是若：theory3、颜色和蓝色的
  - 预测是若：theory0、颜色和红色的
  - 预测是若：theory0、颜色和绿色的
  - 预测是若：theory2、颜色和绿色的
  - theory2的理论规模是4
  - 预测是若：theory2、形状和立方体
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i3、红色的、球体和否
  - 的物品是：i2、绿色的、球体和否
  - 的物品是：i5、蓝色的、球体和是
  - 的物品是：i7、绿色的、圆锥和否
  - 的物品是：i6、绿色的、球体和是
  - 的物品是：i1、绿色的、圆锥和是
  - 的物品是：i4、红色的、球体和是
  - 的物品是：i0、蓝色的、球体和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - 予言するはいもし...:theory1, 形と松毬
  - theory32理論大きさ
  - theory04理論大きさ
  - theory12理論大きさ
  - 予言するはいもし...:theory3, 色と青い
  - 予言するはいもし...:theory0, 色と赤い
  - 予言するはいもし...:theory0, 色と緑の
  - 予言するはいもし...:theory2, 色と緑の
  - theory24理論大きさ
  - 予言するはいもし...:theory2, 形と立方体
費用モデル:例外費やす:2.
資料:
  - 項目:i3, 赤い, 球といいえ
  - 項目:i2, 緑の, 球といいえ
  - 項目:i5, 青い, 球とはい
  - 項目:i7, 緑の, 松毬といいえ
  - 項目:i6, 緑の, 球とはい
  - 項目:i1, 緑の, 松毬とはい
  - 項目:i4, 赤い, 球とはい
  - 項目:i0, 青い, 球とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - öngörür evet eğer: theory1, şekil ve koni
  - theory3 2 kuram boyut
  - theory0 4 kuram boyut
  - theory1 2 kuram boyut
  - öngörür evet eğer: theory3, renk ve mavi
  - öngörür evet eğer: theory0, renk ve kırmızı
  - öngörür evet eğer: theory0, renk ve yeşil
  - öngörür evet eğer: theory2, renk ve yeşil
  - theory2 4 kuram boyut
  - öngörür evet eğer: theory2, şekil ve küp
Cost model: exception maliyet: 2.
Veriler:
  - item: i3, kırmızı, küre ve hayır
  - item: i2, yeşil, küre ve hayır
  - item: i5, mavi, küre ve evet
  - item: i7, yeşil, koni ve hayır
  - item: i6, yeşil, küre ve evet
  - item: i1, yeşil, koni ve evet
  - item: i4, kırmızı, küre ve evet
  - item: i0, mavi, küre ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ مَخْرُوط
  - نَظَرِيَّة مَقَاس theory3 2
  - نَظَرِيَّة مَقَاس theory0 4
  - نَظَرِيَّة مَقَاس theory1 2
  - تَنَبَّأَ نَعَم إِنْ: theory3، لَوْن وَـ أَزْرَق
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَحْمَر
  - تَنَبَّأَ نَعَم إِنْ: theory0، لَوْن وَـ أَخْضَر
  - تَنَبَّأَ نَعَم إِنْ: theory2، لَوْن وَـ أَخْضَر
  - نَظَرِيَّة مَقَاس theory2 4
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ كَعْبَة
cost model: اِسْتِثْنَاء cost 2.
بَيَانَات:
  - بَنْد: i3، أَحْمَر، كُرَة وَـ no
  - بَنْد: i2، أَخْضَر، كُرَة وَـ no
  - بَنْد: i5، أَزْرَق، كُرَة وَـ نَعَم
  - بَنْد: i7، أَخْضَر، مَخْرُوط وَـ no
  - بَنْد: i6، أَخْضَر، كُرَة وَـ نَعَم
  - بَنْد: i1، أَخْضَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i4، أَحْمَر، كُرَة وَـ نَعَم
  - بَنْد: i0، أَزْرَق، كُرَة وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - predict დიახ თუ: theory1, ფორმა და გირჩა
  - theory3 2 თეორია ზომა
  - theory0 4 თეორია ზომა
  - theory1 2 თეორია ზომა
  - predict დიახ თუ: theory3, ფერი და ლურჯი
  - predict დიახ თუ: theory0, ფერი და წითელი
  - predict დიახ თუ: theory0, ფერი და მწვანე
  - predict დიახ თუ: theory2, ფერი და მწვანე
  - theory2 4 თეორია ზომა
  - predict დიახ თუ: theory2, ფორმა და კუბი
Ფასი მოდელი: გამონაკლისი ღირს 2.
Მონაცემები:
  - item: i3, წითელი, სფერო და no
  - item: i2, მწვანე, სფერო და no
  - item: i5, ლურჯი, სფერო და დიახ
  - item: i7, მწვანე, გირჩა და no
  - item: i6, მწვანე, სფერო და დიახ
  - item: i1, მწვანე, გირჩა და დიახ
  - item: i4, წითელი, სფერო და დიახ
  - item: i0, ლურჯი, სფერო და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - matakite āe mēnā: theory1, āhuahanga me koeko
  - ariā size theory3 2
  - ariā size theory0 4
  - ariā size theory1 2
  - matakite āe mēnā: theory3, kano me kahurangi
  - matakite āe mēnā: theory0, kano me red
  - matakite āe mēnā: theory0, kano me kākāriki
  - matakite āe mēnā: theory2, kano me kākāriki
  - ariā size theory2 4
  - matakite āe mēnā: theory2, āhuahanga me mataono
Cost model: hāungatanga cost 2.
Hōtuku:
  - tūemi: i3, red, poi me kāo
  - tūemi: i2, kākāriki, poi me kāo
  - tūemi: i5, kahurangi, poi me āe
  - tūemi: i7, kākāriki, koeko me kāo
  - tūemi: i6, kākāriki, poi me āe
  - tūemi: i1, kākāriki, koeko me āe
  - tūemi: i4, red, poi me āe
  - tūemi: i0, kahurangi, poi me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - dự báo vâng nếu: theory1, hình dáng và nón
  - theory3 lý thuyết kích thước 2
  - theory0 lý thuyết kích thước 4
  - theory1 lý thuyết kích thước 2
  - dự báo vâng nếu: theory3, màu và blue
  - dự báo vâng nếu: theory0, màu và đỏ
  - dự báo vâng nếu: theory0, màu và green
  - dự báo vâng nếu: theory2, màu và green
  - theory2 lý thuyết kích thước 4
  - dự báo vâng nếu: theory2, hình dáng và lập phương
Cost model: lệ ngoại cost 2.
Dữ liệu:
  - vật phẩm: i3, đỏ, mặt cầu và no
  - vật phẩm: i2, green, mặt cầu và no
  - vật phẩm: i5, blue, mặt cầu và vâng
  - vật phẩm: i7, green, nón và no
  - vật phẩm: i6, green, mặt cầu và vâng
  - vật phẩm: i1, green, nón và vâng
  - vật phẩm: i4, đỏ, mặt cầu và vâng
  - vật phẩm: i0, blue, mặt cầu và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและกรวย
  - theory3ทฤษฎีขนาด2
  - theory0ทฤษฎีขนาด4
  - theory1ทฤษฎีขนาด2
  - ทำนายใช่ถ้า หาก:theory3, สีและน้ำเงิน
  - ทำนายใช่ถ้า หาก:theory0, สีและแดง
  - ทำนายใช่ถ้า หาก:theory0, สีและเขียว
  - ทำนายใช่ถ้า หาก:theory2, สีและเขียว
  - theory2ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและลูกบาศก์
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:2.
ข้อมูล:
  - item:i3, แดง, ทรงกลมและไฮ้
  - item:i2, เขียว, ทรงกลมและไฮ้
  - item:i5, น้ำเงิน, ทรงกลมและใช่
  - item:i7, เขียว, กรวยและไฮ้
  - item:i6, เขียว, ทรงกลมและใช่
  - item:i1, เขียว, กรวยและใช่
  - item:i4, แดง, ทรงกลมและใช่
  - item:i0, น้ำเงิน, ทรงกลมและใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - கணி ஆம் என்றால்: theory3, நிறம் மற்றும் நீலம்
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் சிவப்பு
  - கணி ஆம் என்றால்: theory0, நிறம் மற்றும் பச்சை
  - கணி ஆம் என்றால்: theory2, நிறம் மற்றும் பச்சை
  - theory2 4 theory size
  - கணி ஆம் என்றால்: theory2, shape மற்றும் cube
cost model: exception செலவு 2.
தரவுகள்:
  - item: i3, சிவப்பு, sphere மற்றும் no
  - item: i2, பச்சை, sphere மற்றும் no
  - item: i5, நீலம், sphere மற்றும் ஆம்
  - item: i7, பச்சை, cone மற்றும் no
  - item: i6, பச்சை, sphere மற்றும் ஆம்
  - item: i1, பச்சை, cone மற்றும் ஆம்
  - item: i4, சிவப்பு, sphere மற்றும் ஆம்
  - item: i0, நீலம், sphere மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - predict ndiyo kama: theory1, umbo na koni
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict ndiyo kama: theory3, rangi na buluu
  - predict ndiyo kama: theory0, rangi na ekundu
  - predict ndiyo kama: theory0, rangi na kijani
  - predict ndiyo kama: theory2, rangi na kijani
  - theory2 theory size 4
  - predict ndiyo kama: theory2, umbo na mchemraba
Cost model: exception gharimu: 2.
Data:
  - item: i3, ekundu, tufe na hapana
  - item: i2, kijani, tufe na hapana
  - item: i5, buluu, tufe na ndiyo
  - item: i7, kijani, koni na hapana
  - item: i6, kijani, tufe na ndiyo
  - item: i1, kijani, koni na ndiyo
  - item: i4, ekundu, tufe na ndiyo
  - item: i0, buluu, tufe na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict aooʼ ládą́ą́ʼ: theory3, nidaashchʼąąʼígíí dóó dootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó łichííʼ
  - predict aooʼ ládą́ą́ʼ: theory0, nidaashchʼąąʼígíí dóó dinootłʼizh
  - predict aooʼ ládą́ą́ʼ: theory2, nidaashchʼąąʼígíí dóó dinootłʼizh
  - theory2 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó cube
Cost model: exception bą́ą́h ílį́ 2.
Data:
  - item: i3, łichííʼ, sphere dóó no
  - item: i2, dinootłʼizh, sphere dóó no
  - item: i5, dootłʼizh, sphere dóó aooʼ
  - item: i7, dinootłʼizh, cone dóó no
  - item: i6, dinootłʼizh, sphere dóó aooʼ
  - item: i1, dinootłʼizh, cone dóó aooʼ
  - item: i4, łichííʼ, sphere dóó aooʼ
  - item: i0, dootłʼizh, sphere dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - predict aap if: theory1, shape aamma cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict aap if: theory3, qalipaat aamma tungujortoq
  - predict aap if: theory0, qalipaat aamma aappalajaarpoq
  - predict aap if: theory0, qalipaat aamma qorsuk
  - predict aap if: theory2, qalipaat aamma qorsuk
  - theory2 4 theory size
  - predict aap if: theory2, shape aamma cube
Cost model: exception cost 2.
Data:
  - item: i3, aappalajaarpoq, sphere aamma no
  - item: i2, qorsuk, sphere aamma no
  - item: i5, tungujortoq, sphere aamma aap
  - item: i7, qorsuk, cone aamma no
  - item: i6, qorsuk, sphere aamma aap
  - item: i1, qorsuk, cone aamma aap
  - item: i4, aappalajaarpoq, sphere aamma aap
  - item: i0, tungujortoq, sphere aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ya sapos: theory1, shape na cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict ya sapos: theory3, kala na blu
  - predict ya sapos: theory0, kala na ret
  - predict ya sapos: theory0, kala na grinpela
  - predict ya sapos: theory2, kala na grinpela
  - theory2 theory size 4
  - predict ya sapos: theory2, shape na cube
Cost model: exception cost 2.
Data:
  - item: i3, ret, sphere na no
  - item: i2, grinpela, sphere na no
  - item: i5, blu, sphere na ya
  - item: i7, grinpela, cone na no
  - item: i6, grinpela, sphere na ya
  - item: i1, grinpela, cone na ya
  - item: i4, ret, sphere na ya
  - item: i0, blu, sphere na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, color blue
  - predict yes if: theory0, color màxke
  - predict yes if: theory0, color green
  - predict yes if: theory2, color green
  - theory2 theory size 4
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, màxke, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere yes
  - item: i7, green, cone no
  - item: i6, green, sphere yes
  - item: i1, green, cone yes
  - item: i4, màxke, sphere yes
  - item: i0, blue, sphere yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - predict yes if: theory1, shape а cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, бос а blue
  - predict yes if: theory0, бос а цӏе
  - predict yes if: theory0, бос а баьццара
  - predict yes if: theory2, бос а баьццара
  - theory2 theory size 4
  - predict yes if: theory2, shape а cube
Cost model: exception cost 2.
Data:
  - item: i3, цӏе, sphere а no
  - item: i2, баьццара, sphere а no
  - item: i5, blue, sphere а yes
  - item: i7, баьццара, cone а no
  - item: i6, баьццара, sphere а yes
  - item: i1, баьццара, cone а yes
  - item: i4, цӏе, sphere а yes
  - item: i0, blue, sphere а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - 예언하다 네 만약: theory1, 모양 솔방울
  - theory3 2 이론 크기
  - theory0 4 이론 크기
  - theory1 2 이론 크기
  - 예언하다 네 만약: theory3, 색깔 푸르다
  - 예언하다 네 만약: theory0, 색깔 빨갛다
  - 예언하다 네 만약: theory0, 색깔 녹색의
  - 예언하다 네 만약: theory2, 색깔 녹색의
  - theory2 4 이론 크기
  - 예언하다 네 만약: theory2, 모양 정육면체
cost model: 예외 cost 2.
자료:
  - 아이템: i3, 빨갛다, 구체 아니요
  - 아이템: i2, 녹색의, 구체 아니요
  - 아이템: i5, 푸르다, 구체 네
  - 아이템: i7, 녹색의, 솔방울 아니요
  - 아이템: i6, 녹색의, 구체 네
  - 아이템: i1, 녹색의, 솔방울 네
  - 아이템: i4, 빨갛다, 구체 네
  - 아이템: i0, 푸르다, 구체 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - aurresan bai if: theory1, shape eta pinaburu
  - theory3 2 theory tamaina
  - theory0 4 theory tamaina
  - theory1 2 theory tamaina
  - aurresan bai if: theory3, kolore eta urdin
  - aurresan bai if: theory0, kolore eta gorri
  - aurresan bai if: theory0, kolore eta berde
  - aurresan bai if: theory2, kolore eta berde
  - theory2 4 theory tamaina
  - aurresan bai if: theory2, shape eta kubo
Cost model: exception kostatu 2.
Data:
  - item: i3, gorri, esfera eta no
  - item: i2, berde, esfera eta no
  - item: i5, urdin, esfera eta bai
  - item: i7, berde, pinaburu eta no
  - item: i6, berde, esfera eta bai
  - item: i1, berde, pinaburu eta bai
  - item: i4, gorri, esfera eta bai
  - item: i0, urdin, esfera eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict ᎥᎥ if: theory3, ᏗᎧᏃᏗ ᎠᎴ ᏌᎪᏂᎨᎢ
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎩᎦᎨ
  - predict ᎥᎥ if: theory0, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - predict ᎥᎥ if: theory2, ᏗᎧᏃᏗ ᎠᎴ ᎢᏤ ᎢᏳᏍᏗ
  - theory2 theory size 4
  - predict ᎥᎥ if: theory2, shape ᎠᎴ cube
Cost model: exception cost 2.
Data:
  - item: i3, ᎩᎦᎨ, sphere ᎠᎴ ᏝᏃ
  - item: i2, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᏝᏃ
  - item: i5, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᎥᎥ
  - item: i7, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᏝᏃ
  - item: i6, ᎢᏤ ᎢᏳᏍᏗ, sphere ᎠᎴ ᎥᎥ
  - item: i1, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᎥᎥ
  - item: i4, ᎩᎦᎨ, sphere ᎠᎴ ᎥᎥ
  - item: i0, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict arí if: theory1, shape cone
  - theory3 theory chhika 2
  - theory0 theory chhika 4
  - theory1 theory chhika 2
  - predict arí if: theory3, llimphi anqas
  - predict arí if: theory0, llimphi puka
  - predict arí if: theory0, llimphi waylla
  - predict arí if: theory2, llimphi waylla
  - theory2 theory chhika 4
  - predict arí if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, puka, sphere mánan
  - item: i2, waylla, sphere mánan
  - item: i5, anqas, sphere arí
  - item: i7, waylla, cone mánan
  - item: i6, waylla, sphere arí
  - item: i1, waylla, cone arí
  - item: i4, puka, sphere arí
  - item: i0, anqas, sphere arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape ыкӏи cone
  - theory3 2 тиориэ size
  - theory0 4 тиориэ size
  - theory1 2 тиориэ size
  - predict yes if: theory3, color ыкӏи blue
  - predict yes if: theory0, color ыкӏи плъыжьы
  - predict yes if: theory0, color ыкӏи уцышъо
  - predict yes if: theory2, color ыкӏи уцышъо
  - theory2 4 тиориэ size
  - predict yes if: theory2, shape ыкӏи cube
Cost model: exception cost 2.
Data:
  - item: i3, плъыжьы, sphere ыкӏи no
  - item: i2, уцышъо, sphere ыкӏи no
  - item: i5, blue, sphere ыкӏи yes
  - item: i7, уцышъо, cone ыкӏи no
  - item: i6, уцышъо, sphere ыкӏи yes
  - item: i1, уцышъо, cone ыкӏи yes
  - item: i4, плъыжьы, sphere ыкӏи yes
  - item: i0, blue, sphere ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - predict yes if: theory1, shape thiab cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, xim thiab xiav
  - predict yes if: theory0, xim thiab liab
  - predict yes if: theory0, xim thiab ntsuab
  - predict yes if: theory2, xim thiab ntsuab
  - theory2 theory size 4
  - predict yes if: theory2, shape thiab cube
Cost model: exception nqi 2.
Data:
  - item: i3, liab, sphere thiab no
  - item: i2, ntsuab, sphere thiab no
  - item: i5, xiav, sphere thiab yes
  - item: i7, ntsuab, cone thiab no
  - item: i6, ntsuab, sphere thiab yes
  - item: i1, ntsuab, cone thiab yes
  - item: i4, liab, sphere thiab yes
  - item: i0, xiav, sphere thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - predict ngai if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict ngai if: theory3, color blue
  - predict ngai if: theory0, color red
  - predict ngai if: theory0, color green
  - predict ngai if: theory2, color green
  - theory2 theory size 4
  - predict ngai if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere woi
  - item: i2, green, sphere woi
  - item: i5, blue, sphere ngai
  - item: i7, green, cone woi
  - item: i6, green, sphere ngai
  - item: i1, green, cone ngai
  - item: i4, red, sphere ngai
  - item: i0, blue, sphere ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - predict i̠i if: theory1, shape micha' cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict i̠i if: theory3, color micha' blue
  - predict i̠i if: theory0, color micha' homa
  - predict i̠i if: theory0, color micha' green
  - predict i̠i if: theory2, color micha' green
  - theory2 theory size 4
  - predict i̠i if: theory2, shape micha' cube
Cost model: exception cost 2.
Data:
  - item: i3, homa, sphere micha' no
  - item: i2, green, sphere micha' no
  - item: i5, blue, sphere micha' i̠i
  - item: i7, green, cone micha' no
  - item: i6, green, sphere micha' i̠i
  - item: i1, green, cone micha' i̠i
  - item: i4, homa, sphere micha' i̠i
  - item: i0, blue, sphere micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - predict yes if: theory1, shape カ cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict yes if: theory3, color カ blue
  - predict yes if: theory0, color カ フレ
  - predict yes if: theory0, color カ green
  - predict yes if: theory2, color カ green
  - theory2 4 theory size
  - predict yes if: theory2, shape カ cube
Cost model: exception cost 2.
Data:
  - item: i3, フレ, sphere カ ソモ
  - item: i2, green, sphere カ ソモ
  - item: i5, blue, sphere カ yes
  - item: i7, green, cone カ ソモ
  - item: i6, green, sphere カ yes
  - item: i1, green, cone カ yes
  - item: i4, フレ, sphere カ yes
  - item: i0, blue, sphere カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes эвыр: theory1, shape ынкъам cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes эвыр: theory3, пэран ынкъам ныԓиԓиԓӄин
  - predict yes эвыр: theory0, пэран ынкъам чеԓгыԓьын
  - predict yes эвыр: theory0, пэран ынкъам green
  - predict yes эвыр: theory2, пэран ынкъам green
  - theory2 theory size 4
  - predict yes эвыр: theory2, shape ынкъам cube
Cost model: exception cost 2.
Data:
  - item: i3, чеԓгыԓьын, sphere ынкъам no
  - item: i2, green, sphere ынкъам no
  - item: i5, ныԓиԓиԓӄин, sphere ынкъам yes
  - item: i7, green, cone ынкъам no
  - item: i6, green, sphere ынкъам yes
  - item: i1, green, cone ынкъам yes
  - item: i4, чеԓгыԓьын, sphere ынкъам yes
  - item: i0, ныԓиԓиԓӄин, sphere ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes wa: theory1, shape yéetel cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes wa: theory3, color yéetel blue
  - predict yes wa: theory0, color yéetel chak
  - predict yes wa: theory0, color yéetel yaʼax
  - predict yes wa: theory2, color yéetel yaʼax
  - theory2 theory size 4
  - predict yes wa: theory2, shape yéetel cube
Cost model: exception cost 2.
Data:
  - item: i3, chak, sphere yéetel no
  - item: i2, yaʼax, sphere yéetel no
  - item: i5, blue, sphere yéetel yes
  - item: i7, yaʼax, cone yéetel no
  - item: i6, yaʼax, sphere yéetel yes
  - item: i1, yaʼax, cone yéetel yes
  - item: i4, chak, sphere yéetel yes
  - item: i0, blue, sphere yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict hą if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict hą if: theory3, color blue
  - predict hą if: theory0, color red
  - predict hą if: theory0, color green
  - predict hą if: theory2, color green
  - theory2 theory size 4
  - predict hą if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere hą
  - item: i7, green, cone no
  - item: i6, green, sphere hą
  - item: i1, green, cone hą
  - item: i4, red, sphere hą
  - item: i0, blue, sphere hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - predict yes if: theory1, shape na cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict yes if: theory3, color na blue
  - predict yes if: theory0, color na red
  - predict yes if: theory0, color na green
  - predict yes if: theory2, color na green
  - theory2 4 theory size
  - predict yes if: theory2, shape na cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere na no
  - item: i2, green, sphere na no
  - item: i5, blue, sphere na yes
  - item: i7, green, cone na no
  - item: i6, green, sphere na yes
  - item: i1, green, cone na yes
  - item: i4, red, sphere na yes
  - item: i0, blue, sphere na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict heẽ if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict heẽ if: theory3, color blue
  - predict heẽ if: theory0, color pytã
  - predict heẽ if: theory0, color green
  - predict heẽ if: theory2, color green
  - theory2 theory size 4
  - predict heẽ if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, pytã, sphere nahániri
  - item: i2, green, sphere nahániri
  - item: i5, blue, sphere heẽ
  - item: i7, green, cone nahániri
  - item: i6, green, sphere heẽ
  - item: i1, green, cone heẽ
  - item: i4, pytã, sphere heẽ
  - item: i0, blue, sphere heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict jisa if: theory1, shape cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict jisa if: theory3, sami larama
  - predict jisa if: theory0, sami chupika
  - predict jisa if: theory0, sami ch’uxña
  - predict jisa if: theory2, sami ch’uxña
  - theory2 4 theory size
  - predict jisa if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, chupika, sphere no
  - item: i2, ch’uxña, sphere no
  - item: i5, larama, sphere jisa
  - item: i7, ch’uxña, cone no
  - item: i6, ch’uxña, sphere jisa
  - item: i1, ch’uxña, cone jisa
  - item: i4, chupika, sphere jisa
  - item: i0, larama, sphere jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ʻée if: theory1, shape ǂʻá cone
  - theory3 theory ʻǁnàhã 2
  - theory0 theory ʻǁnàhã 4
  - theory1 theory ʻǁnàhã 2
  - predict ʻée if: theory3, color ǂʻá blue
  - predict ʻée if: theory0, color ǂʻá red
  - predict ʻée if: theory0, color ǂʻá green
  - predict ʻée if: theory2, color ǂʻá green
  - theory2 theory ʻǁnàhã 4
  - predict ʻée if: theory2, shape ǂʻá cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere ǂʻá ʻĩĩ
  - item: i2, green, sphere ǂʻá ʻĩĩ
  - item: i5, blue, sphere ǂʻá ʻée
  - item: i7, green, cone ǂʻá ʻĩĩ
  - item: i6, green, sphere ǂʻá ʻée
  - item: i1, green, cone ǂʻá ʻée
  - item: i4, red, sphere ǂʻá ʻée
  - item: i0, blue, sphere ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict yes if: theory3, вал blue
  - predict yes if: theory0, вал red
  - predict yes if: theory0, вал green
  - predict yes if: theory2, вал green
  - theory2 4 theory size
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere yes
  - item: i7, green, cone no
  - item: i6, green, sphere yes
  - item: i1, green, cone yes
  - item: i4, red, sphere yes
  - item: i0, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, color blue
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - predict yes if: theory2, color green
  - theory2 theory size 4
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere yes
  - item: i7, green, cone no
  - item: i6, green, sphere yes
  - item: i1, green, cone yes
  - item: i4, red, sphere yes
  - item: i0, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict yes if: theory3, color blue
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - predict yes if: theory2, color green
  - theory2 4 theory size
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere yes
  - item: i7, green, cone no
  - item: i6, green, sphere yes
  - item: i1, green, cone yes
  - item: i4, red, sphere yes
  - item: i0, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict ee if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict ee if: theory3, color blue
  - predict ee if: theory0, color red
  - predict ee if: theory0, color green
  - predict ee if: theory2, color green
  - theory2 theory size 4
  - predict ee if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere ee
  - item: i7, green, cone no
  - item: i6, green, sphere ee
  - item: i1, green, cone ee
  - item: i4, red, sphere ee
  - item: i0, blue, sphere ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, color blue
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - predict yes if: theory2, color green
  - theory2 theory size 4
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere inte
  - item: i2, green, sphere inte
  - item: i5, blue, sphere yes
  - item: i7, green, cone inte
  - item: i6, green, sphere yes
  - item: i1, green, cone yes
  - item: i4, red, sphere yes
  - item: i0, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - prédire oui si: theory1, forme et cône
  - theory3 théorie taille 2
  - theory0 théorie taille 4
  - theory1 théorie taille 2
  - prédire oui si: theory3, couleur et bleu
  - prédire oui si: theory0, couleur et rouge
  - prédire oui si: theory0, couleur et vert
  - prédire oui si: theory2, couleur et vert
  - theory2 théorie taille 4
  - prédire oui si: theory2, forme et cube
Coûts : exception coûter 2.
Les données sont:
  - article: i3, rouge, sphère et no
  - article: i2, vert, sphère et no
  - article: i5, bleu, sphère et oui
  - article: i7, vert, cône et no
  - article: i6, vert, sphère et oui
  - article: i1, vert, cône et oui
  - article: i4, rouge, sphère et oui
  - article: i0, bleu, sphère et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - predict jes se: theory1, formi kaj strobilo
  - theory3 teorio grandeco 2
  - theory0 teorio grandeco 4
  - theory1 teorio grandeco 2
  - predict jes se: theory3, koloro kaj blua
  - predict jes se: theory0, koloro kaj ruĝa
  - predict jes se: theory0, koloro kaj verda
  - predict jes se: theory2, koloro kaj verda
  - theory2 teorio grandeco 4
  - predict jes se: theory2, formi kaj kubo
Kosto pozistino: escepto kosti 2.
Datumo:
  - ero: i3, ruĝa, sfero kaj no
  - ero: i2, verda, sfero kaj no
  - ero: i5, blua, sfero kaj jes
  - ero: i7, verda, strobilo kaj no
  - ero: i6, verda, sfero kaj jes
  - ero: i1, verda, strobilo kaj jes
  - ero: i4, ruĝa, sfero kaj jes
  - ero: i0, blua, sfero kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - megjósol igen ha: theory1, alak és toboz
  - theory3 elmélet méret 2
  - theory0 elmélet méret 4
  - theory1 elmélet méret 2
  - megjósol igen ha: theory3, szín és kék
  - megjósol igen ha: theory0, szín és piros
  - megjósol igen ha: theory0, szín és zöld
  - megjósol igen ha: theory2, szín és zöld
  - theory2 elmélet méret 4
  - megjósol igen ha: theory2, alak és kocka
Költség manöken: kivétel kerül 2.
Adat:
  - cikk: i3, piros, gömb és no
  - cikk: i2, zöld, gömb és no
  - cikk: i5, kék, gömb és igen
  - cikk: i7, zöld, toboz és no
  - cikk: i6, zöld, gömb és igen
  - cikk: i1, zöld, toboz és igen
  - cikk: i4, piros, gömb és igen
  - cikk: i0, kék, gömb és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - predict係要是:theory1, 形狀及cone
  - theory3theory size2
  - theory0theory size4
  - theory1theory size2
  - predict係要是:theory3, 色及藍色嘅
  - predict係要是:theory0, 色及紅
  - predict係要是:theory0, 色及綠色嘅
  - predict係要是:theory2, 色及綠色嘅
  - theory2theory size4
  - predict係要是:theory2, 形狀及cube
cost model:exception cost:2.
數據:
  - item:i3, 紅, sphere及no
  - item:i2, 綠色嘅, sphere及no
  - item:i5, 藍色嘅, sphere及係
  - item:i7, 綠色嘅, cone及no
  - item:i6, 綠色嘅, sphere及係
  - item:i1, 綠色嘅, cone及係
  - item:i4, 紅, sphere及係
  - item:i0, 藍色嘅, sphere及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - predict yes if: theory1, shape とぅ cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, color とぅ blue
  - predict yes if: theory0, color とぅ red
  - predict yes if: theory0, color とぅ green
  - predict yes if: theory2, color とぅ green
  - theory2 theory size 4
  - predict yes if: theory2, shape とぅ cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere とぅ no
  - item: i2, green, sphere とぅ no
  - item: i5, blue, sphere とぅ yes
  - item: i7, green, cone とぅ no
  - item: i6, green, sphere とぅ yes
  - item: i1, green, cone とぅ yes
  - item: i4, red, sphere とぅ yes
  - item: i0, blue, sphere とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - predict иә егер: theory1, нысан және бүршік
  - theory3 теория мөлшер 2
  - theory0 теория мөлшер 4
  - theory1 теория мөлшер 2
  - predict иә егер: theory3, түс және көк
  - predict иә егер: theory0, түс және қызыл
  - predict иә егер: theory0, түс және жасыл
  - predict иә егер: theory2, түс және жасыл
  - theory2 теория мөлшер 4
  - predict иә егер: theory2, нысан және куб
Тұру үлгі: ерекшелік тұру 2.
Деректер:
  - зат: i3, қызыл, сфера және жоқ
  - зат: i2, жасыл, сфера және жоқ
  - зат: i5, көк, сфера және иә
  - зат: i7, жасыл, бүршік және жоқ
  - зат: i6, жасыл, сфера және иә
  - зат: i1, жасыл, бүршік және иә
  - зат: i4, қызыл, сфера және иә
  - зат: i0, көк, сфера және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - predict כֵּן אִם: theory1, צוּרָה וְ־ אצטרובל
  - theory3 תּוֹרָה גֹּדֶל 2
  - theory0 תּוֹרָה גֹּדֶל 4
  - theory1 תּוֹרָה גֹּדֶל 2
  - predict כֵּן אִם: theory3, צֶבַע וְ־ כָּחוֹל
  - predict כֵּן אִם: theory0, צֶבַע וְ־ אָדוֹם
  - predict כֵּן אִם: theory0, צֶבַע וְ־ ירוק מקנאה
  - predict כֵּן אִם: theory2, צֶבַע וְ־ ירוק מקנאה
  - theory2 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory2, צוּרָה וְ־ קובייה
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 2.
נְתוּנִים:
  - אבזר: i3, אָדוֹם, כַּדּוּר וְ־ no
  - אבזר: i2, ירוק מקנאה, כַּדּוּר וְ־ no
  - אבזר: i5, כָּחוֹל, כַּדּוּר וְ־ כֵּן
  - אבזר: i7, ירוק מקנאה, אצטרובל וְ־ no
  - אבזר: i6, ירוק מקנאה, כַּדּוּר וְ־ כֵּן
  - אבזר: i1, ירוק מקנאה, אצטרובל וְ־ כֵּן
  - אבזר: i4, אָדוֹם, כַּדּוּר וְ־ כֵּן
  - אבזר: i0, כָּחוֹל, כַּדּוּר וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?

Candidates:
  - predict იახ if: theory1, shape დო cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict იახ if: theory3, color დო blue
  - predict იახ if: theory0, color დო red
  - predict იახ if: theory0, color დო green
  - predict იახ if: theory2, color დო green
  - theory2 theory size 4
  - predict იახ if: theory2, shape დო cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere დო no
  - item: i2, green, sphere დო no
  - item: i5, blue, sphere დო იახ
  - item: i7, green, cone დო no
  - item: i6, green, sphere დო იახ
  - item: i1, green, cone დო იახ
  - item: i4, red, sphere დო იახ
  - item: i0, blue, sphere დო იახ
Rules:
  - rule theory predict იახ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Kandidat:
  - predict ya jika: theory1, bentuk dan kerucut
  - theory3 teori ukuran 2
  - theory0 teori ukuran 4
  - theory1 teori ukuran 2
  - predict ya jika: theory3, warna dan biru
  - predict ya jika: theory0, warna dan merah
  - predict ya jika: theory0, warna dan hijau
  - predict ya jika: theory2, warna dan hijau
  - theory2 teori ukuran 4
  - predict ya jika: theory2, bentuk dan kubus
Cost model: pengecualian menghabiskan biaya 2.
Data:
  - benda: i3, merah, bola dan no
  - benda: i2, hijau, bola dan no
  - benda: i5, biru, bola dan ya
  - benda: i7, hijau, kerucut dan no
  - benda: i6, hijau, bola dan ya
  - benda: i1, hijau, kerucut dan ya
  - benda: i4, merah, bola dan ya
  - benda: i0, biru, bola dan ya
Rules:
  - rule teori predict ya untuk sebuah benda iff beberapa dari its conditions matches bahwa benda
  - rule sebuah pengecualian berada sebuah benda punya siapa prediksi differs from its label
  - rule jumlah menghabiskan biaya berada teori ukuran plus pengecualian menghabiskan biaya kali angka dari exceptions
  - rule memilih si teori dari least jumlah menghabiskan biaya
Mdl optimal teori apa?

បេក្ខជន:
  - ចាប់យាមច៎ាបើ:theory1, គ្រោងនឹងកោណ
  - theory3ទ្រឹស្ដីទំហំ2
  - theory0ទ្រឹស្ដីទំហំ4
  - theory1ទ្រឹស្ដីទំហំ2
  - ចាប់យាមច៎ាបើ:theory3, ពណ៌នឹងខៀវ
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងក្រហម
  - ចាប់យាមច៎ាបើ:theory0, ពណ៌នឹងបៃតង
  - ចាប់យាមច៎ាបើ:theory2, ពណ៌នឹងបៃតង
  - theory2ទ្រឹស្ដីទំហំ4
  - ចាប់យាមច៎ាបើ:theory2, គ្រោងនឹងគូប
ថ្លៃម៉ូដែល:exceptionមានតម្លៃ:2.
ទិន្នន័យ:
  - item:i3, ក្រហម, គោលនឹងទេ
  - item:i2, បៃតង, គោលនឹងទេ
  - item:i5, ខៀវ, គោលនឹងច៎ា
  - item:i7, បៃតង, កោណនឹងទេ
  - item:i6, បៃតង, គោលនឹងច៎ា
  - item:i1, បៃតង, កោណនឹងច៎ា
  - item:i4, ក្រហម, គោលនឹងច៎ា
  - item:i0, ខៀវ, គោលនឹងច៎ា
ក្បួន:
  - ក្បួន:ទ្រឹស្ដីចាប់យាមច៎ាសម្រាប់anitemiffអ្នកខ្លះនៃitsconditionsmatchesថាitem
  - ក្បួន:anexceptionត្រូវបានanitemwhosepredictiondiffersពីitsស្លាក
  - ក្បួន:totalមានតម្លៃត្រូវបានទ្រឹស្ដីទំហំបូកexceptionមានតម្លៃគុណលេខនៃexceptions
  - ក្បួន:រើសtheទ្រឹស្ដីនៃleasttotalមានតម្លៃ
mdloptimalទ្រឹស្ដីអី?

ຜູ້ສະໝັກ:
  - ທຳນວາຍແມ່ນຖ້າ:theory1, ຮູບຮ່າງແລະcone
  - theory3ທິດສະດີຂະຫນາດ2
  - theory0ທິດສະດີຂະຫນາດ4
  - theory1ທິດສະດີຂະຫນາດ2
  - ທຳນວາຍແມ່ນຖ້າ:theory3, ສີແລະຟ້າ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະແດງ
  - ທຳນວາຍແມ່ນຖ້າ:theory0, ສີແລະຂຽວ
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ສີແລະຂຽວ
  - theory2ທິດສະດີຂະຫນາດ4
  - ທຳນວາຍແມ່ນຖ້າ:theory2, ຮູບຮ່າງແລະລູກບາດ
ຄ່າໃຊ້ຈ່າຍນາງແບບ:exceptionຄ່າໃຊ້ຈ່າຍ:2.
ຂໍ້ມູນ:
  - item:i3, ແດງ, ຄະດີແລະno
  - item:i2, ຂຽວ, ຄະດີແລະno
  - item:i5, ຟ້າ, ຄະດີແລະແມ່ນ
  - item:i7, ຂຽວ, coneແລະno
  - item:i6, ຂຽວ, ຄະດີແລະແມ່ນ
  - item:i1, ຂຽວ, coneແລະແມ່ນ
  - item:i4, ແດງ, ຄະດີແລະແມ່ນ
  - item:i0, ຟ້າ, ຄະດີແລະແມ່ນ
ກະຕິກາ:
  - ກະຕິກາ:ທິດສະດີທຳນວາຍແມ່ນສຳລັບanitemiffsomeຂອງitsconditionsmatchesນັ້ນitem
  - ກະຕິກາ:anexceptionຢູ່anitemwhosepredictiondiffersຈາກitsສລາກ
  - ກະຕິກາ:totalຄ່າໃຊ້ຈ່າຍຢູ່ທິດສະດີຂະຫນາດplusexceptionຄ່າໃຊ້ຈ່າຍtimesເລກຂອງexceptions
  - ກະຕິກາ:ເລືອກທິດສະດີຂອງleasttotalຄ່າໃຊ້ຈ່າຍ
mdloptimalທິດສະດີຫຍັງ?

candidates:
  - predict అవును అయితే: theory1, ఆకారం మరియు cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict అవును అయితే: theory3, రంగు మరియు నీలము
  - predict అవును అయితే: theory0, రంగు మరియు ఎర్ర
  - predict అవును అయితే: theory0, రంగు మరియు పచ్చని
  - predict అవును అయితే: theory2, రంగు మరియు పచ్చని
  - theory2 4 theory size
  - predict అవును అయితే: theory2, ఆకారం మరియు cube
cost model: exception cost 2.
data:
  - item: i3, ఎర్ర, గోళము మరియు no
  - item: i2, పచ్చని, గోళము మరియు no
  - item: i5, నీలము, గోళము మరియు అవును
  - item: i7, పచ్చని, cone మరియు no
  - item: i6, పచ్చని, గోళము మరియు అవును
  - item: i1, పచ్చని, cone మరియు అవును
  - item: i4, ఎర్ర, గోళము మరియు అవును
  - item: i0, నీలము, గోళము మరియు అవును
నియమం:
  - నియమం theory predict అవును కోసము ఒక item iff some of its conditions matches ఆ item
  - నియమం ఒక exception ఉండు ఒక item whose prediction differs from its label
  - నియమం మొత్తము cost ఉండు theory size plus exception cost times సంఖ్య of exceptions
  - నియమం choose theory of least మొత్తము cost
mdl optimal theory ఏమిటి?

Candidates:
  - predict hẹn if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict hẹn if: theory3, color dúdú
  - predict hẹn if: theory0, color pupa
  - predict hẹn if: theory0, color green
  - predict hẹn if: theory2, color green
  - theory2 theory size 4
  - predict hẹn if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, pupa, sphere rárá
  - item: i2, green, sphere rárá
  - item: i5, dúdú, sphere bẹ́ẹ̀ ni
  - item: i7, green, cone rárá
  - item: i6, green, sphere bẹ́ẹ̀ ni
  - item: i1, green, cone bẹ́ẹ̀ ni
  - item: i4, pupa, sphere bẹ́ẹ̀ ni
  - item: i0, dúdú, sphere bẹ́ẹ̀ ni
Rules:
  - rule theory predict hẹn for an item iff àwọn of its conditions matches that item
  - rule an exception wà an item whose prediction differs from its label
  - rule total cost wà theory size plus exception cost times nọ́ḿbà of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - predict au if: theory1, shape cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict au if: theory3, color blue
  - predict au if: theory0, color łichíí
  - predict au if: theory0, color green
  - predict au if: theory2, color green
  - theory2 4 theory size
  - predict au if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, łichíí, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere au
  - item: i7, green, cone no
  - item: i6, green, sphere au
  - item: i1, green, cone au
  - item: i4, łichíí, sphere au
  - item: i0, blue, sphere au
Rules:
  - rule theory predict au for an item iff some of its conditions matches that item
  - rule an exception naiágúóli an item whose prediction differs from its label
  - rule total cost naiágúóli theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

candidates:
  - predict yes if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, color blue
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - predict yes if: theory2, color green
  - theory2 theory size 4
  - predict yes if: theory2, shape cube
cost model: exception cost 2.
data:
  - item: i3, red, sphere ᐋᒃᑲ
  - item: i2, green, sphere ᐋᒃᑲ
  - item: i5, blue, sphere yes
  - item: i7, green, cone ᐋᒃᑲ
  - item: i6, green, sphere yes
  - item: i1, green, cone yes
  - item: i4, red, sphere yes
  - item: i0, blue, sphere yes
rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, color blaw
  - predict yes if: theory0, color red
  - predict yes if: theory0, color grun
  - predict yes if: theory2, color grun
  - theory2 theory size 4
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, grun, sphere no
  - item: i5, blaw, sphere yes
  - item: i7, grun, cone no
  - item: i6, grun, sphere yes
  - item: i1, grun, cone yes
  - item: i4, red, sphere yes
  - item: i0, blaw, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 theory size 2
  - theory0 theory size 4
  - theory1 theory size 2
  - predict yes if: theory3, color blue
  - predict yes if: theory0, color red
  - predict yes if: theory0, color green
  - predict yes if: theory2, color green
  - theory2 theory size 4
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, green, sphere no
  - item: i5, blue, sphere yes
  - item: i7, green, cone no
  - item: i6, green, sphere yes
  - item: i1, green, cone yes
  - item: i4, red, sphere yes
  - item: i0, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - predict yes if: theory1, shape cone
  - theory3 2 theory size
  - theory0 4 theory size
  - theory1 2 theory size
  - predict yes if: theory3, ранг blue
  - predict yes if: theory0, ранг red
  - predict yes if: theory0, ранг къацу
  - predict yes if: theory2, ранг къацу
  - theory2 4 theory size
  - predict yes if: theory2, shape cube
Cost model: exception cost 2.
Data:
  - item: i3, red, sphere no
  - item: i2, къацу, sphere no
  - item: i5, blue, sphere yes
  - item: i7, къацу, cone no
  - item: i6, къацу, sphere yes
  - item: i1, къацу, cone yes
  - item: i4, red, sphere yes
  - item: i0, blue, sphere yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory вуч?

The candidates are:
  - theory2 theory size 2
  - theory0 theory size 4
  - predicts yes if: theory0, shape and cone
  - predicts yes if: theory3, shape and sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predicts yes if: theory2, shape and cone
  - predicts yes if: theory1, shape and cube
  - predicts yes if: theory1, shape and cone
  - predicts yes if: theory3, shape and cone
  - predicts yes if: theory0, shape and cube
Costs: exception cost 2.
The data are:
  - item: i7, red, cone and no
  - item: i0, blue, sphere and no
  - item: i4, red, cube and no
  - item: i1, green, cone and no
  - item: i6, red, cube and no
  - item: i5, green, cone and no
  - item: i3, red, cone and yes
  - item: i2, red, cone and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

Los candidatos son:
  - theory2 el tamaño de la teoría 2
  - theory0 el tamaño de la teoría 4
  - predice sí si: theory0, forma y cono
  - predice sí si: theory3, forma y esfera
  - theory3 el tamaño de la teoría 4
  - theory1 el tamaño de la teoría 4
  - predice sí si: theory2, forma y cono
  - predice sí si: theory1, forma y cubo
  - predice sí si: theory1, forma y cono
  - predice sí si: theory3, forma y cono
  - predice sí si: theory0, forma y cubo
Costes: exception el coste: 2.
Los datos son:
  - el elemento: i7, rojo, cono y no
  - el elemento: i0, azul, esfera y no
  - el elemento: i4, rojo, cubo y no
  - el elemento: i1, verde, cono y no
  - el elemento: i6, rojo, cubo y no
  - el elemento: i5, verde, cono y no
  - el elemento: i3, rojo, cono y sí
  - el elemento: i2, rojo, cono y sí
Las reglas son:
  - la regla: a la teoría predice sí for an el elemento si y sólo si algunos de its conditions coincide con that el elemento
  - la regla: an exception es an el elemento whose prediction differs from its label
  - la regla: total el coste de es la teoría tamaño plus exception el coste de por número de exceptions
  - la regla: choose el la teoría de least total el coste de
¿Qué mdl optimal la teoría?

候选项如下：
  - theory2的理论规模是2
  - theory0的理论规模是4
  - 预测是若：theory0、形状和圆锥
  - 预测是若：theory3、形状和球体
  - theory3的理论规模是4
  - theory1的理论规模是4
  - 预测是若：theory2、形状和圆锥
  - 预测是若：theory1、形状和立方体
  - 预测是若：theory1、形状和圆锥
  - 预测是若：theory3、形状和圆锥
  - 预测是若：theory0、形状和立方体
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i7、红色的、圆锥和否
  - 的物品是：i0、蓝色的、球体和否
  - 的物品是：i4、红色的、立方体和否
  - 的物品是：i1、绿色的、圆锥和否
  - 的物品是：i6、红色的、立方体和否
  - 的物品是：i5、绿色的、圆锥和否
  - 的物品是：i3、红色的、圆锥和是
  - 的物品是：i2、红色的、圆锥和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

Adaylar:
  - theory2 2 kuram boyut
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, şekil ve koni
  - öngörür evet eğer: theory3, şekil ve küre
  - theory3 4 kuram boyut
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory2, şekil ve koni
  - öngörür evet eğer: theory1, şekil ve küp
  - öngörür evet eğer: theory1, şekil ve koni
  - öngörür evet eğer: theory3, şekil ve koni
  - öngörür evet eğer: theory0, şekil ve küp
Cost model: exception maliyet: 2.
Veriler:
  - item: i7, kırmızı, koni ve hayır
  - item: i0, mavi, küre ve hayır
  - item: i4, kırmızı, küp ve hayır
  - item: i1, yeşil, koni ve hayır
  - item: i6, kırmızı, küp ve hayır
  - item: i5, yeşil, koni ve hayır
  - item: i3, kırmızı, koni ve evet
  - item: i2, kırmızı, koni ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

Wagombea ni:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ndiyo kama: theory0, umbo na koni
  - predict ndiyo kama: theory3, umbo na tufe
  - theory3 theory size 4
  - theory1 theory size 4
  - predict ndiyo kama: theory2, umbo na koni
  - predict ndiyo kama: theory1, umbo na mchemraba
  - predict ndiyo kama: theory1, umbo na koni
  - predict ndiyo kama: theory3, umbo na koni
  - predict ndiyo kama: theory0, umbo na mchemraba
Cost model: exception gharimu: 2.
Data:
  - item: i7, ekundu, koni na hapana
  - item: i0, buluu, tufe na hapana
  - item: i4, ekundu, mchemraba na hapana
  - item: i1, kijani, koni na hapana
  - item: i6, ekundu, mchemraba na hapana
  - item: i5, kijani, koni na hapana
  - item: i3, ekundu, koni na ndiyo
  - item: i2, ekundu, koni na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

The candidates are:
  - theory2 theory size 2
  - theory0 theory size 4
  - predicts yes if: theory0, shape and cone
  - predicts yes if: theory3, shape and sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predicts yes if: theory2, shape and cone
  - predicts yes if: theory1, shape and cube
  - predicts yes if: theory1, shape and cone
  - predicts yes if: theory3, shape and cone
  - predicts yes if: theory0, shape and cube
Costs: exception cost 2.
The data are:
  - item: i7, red, cone and no
  - item: i0, blue, sphere and no
  - item: i4, red, cube and no
  - item: i1, green, cone and no
  - item: i6, red, cube and no
  - item: i5, green, cone and no
  - item: i3, red, cone and yes
  - item: i2, red, cone and yes
The rules are:
  - rule a theory predicts yes for an item if and only if some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
What is the mdl optimal theory?

{candidates: [(theory_size theory2 2) (theory_size theory0 4) (predicts_yes_if theory0 shape cone) (predicts_yes_if theory3 shape sphere) (theory_size theory3 4) (theory_size theory1 4) (predicts_yes_if theory2 shape cone) (predicts_yes_if theory1 shape cube) (predicts_yes_if theory1 shape cone) (predicts_yes_if theory3 shape cone) (predicts_yes_if theory0 shape cube)], cost_model: [(exception_cost 2)], data: [(item i7 red cone no) (item i0 blue sphere no) (item i4 red cube no) (item i1 green cone no) (item i6 red cube no) (item i5 green cone no) (item i3 red cone yes) (item i2 red cone yes)], query: mdl_optimal_theory, rules: [(rule (a_theory_predicts_yes_for_an_item_iff_some_of_its_conditions_matches_that_item)) (rule (an_exception_is_an_item_whose_prediction_differs_from_its_label)) (rule (total_cost_is_theory_size_plus_exception_cost_times_number_of_exceptions)) (rule (choose_the_theory_of_least_total_cost))]}

Die Kandidaten sind:
  - theory2 Theorie Größe 2
  - theory0 Theorie Größe 4
  - vorhersagen ja wenn: theory0, Form und Kegel
  - vorhersagen ja wenn: theory3, Form und Kugel
  - theory3 Theorie Größe 4
  - theory1 Theorie Größe 4
  - vorhersagen ja wenn: theory2, Form und Kegel
  - vorhersagen ja wenn: theory1, Form und Kubus
  - vorhersagen ja wenn: theory1, Form und Kegel
  - vorhersagen ja wenn: theory3, Form und Kegel
  - vorhersagen ja wenn: theory0, Form und Kubus
Kosten: Ausnahme kosten 2.
Die Daten sind:
  - Ding: i7, rot, Kegel und nein
  - Ding: i0, blau, Kugel und nein
  - Ding: i4, rot, Kubus und nein
  - Ding: i1, grün, Kegel und nein
  - Ding: i6, rot, Kubus und nein
  - Ding: i5, grün, Kegel und nein
  - Ding: i3, rot, Kegel und ja
  - Ding: i2, rot, Kegel und ja
Die Regeln sind:
  - Regel ein Theorie vorhersagen ja da ein Ding gdw. einige aus sein conditions matches dass Ding
  - Regel ein Ausnahme werden ein Ding wessen Voraussage differs von sein Etikett
  - Regel Gesamtbetrag kosten werden Theorie Größe plus Ausnahme kosten mal Zahl aus exceptions
  - Regel wählen der Theorie aus geringst Gesamtbetrag kosten
Was mdl optimal Theorie?

Кандѝда̄т:
  - theory2 тѐо̄рија величѝна 2
  - theory0 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory0, о̏блӣк и stožac
  - predvidjeti да ȁko: theory3, о̏блӣк и сфе́ра
  - theory3 тѐо̄рија величѝна 4
  - theory1 тѐо̄рија величѝна 4
  - predvidjeti да ȁko: theory2, о̏блӣк и stožac
  - predvidjeti да ȁko: theory1, о̏блӣк и коцка
  - predvidjeti да ȁko: theory1, о̏блӣк и stožac
  - predvidjeti да ȁko: theory3, о̏блӣк и stožac
  - predvidjeti да ȁko: theory0, о̏блӣк и коцка
Trošak мо̀дел: изузе́так ко̏штати 2.
Податци:
  - item: i7, цр̀вен, stožac и no
  - item: i0, пла̑в, сфе́ра и no
  - item: i4, цр̀вен, коцка и no
  - item: i1, зѐлен, stožac и no
  - item: i6, цр̀вен, коцка и no
  - item: i5, зѐлен, stožac и no
  - item: i3, цр̀вен, stožac и да
  - item: i2, цр̀вен, stožac и да
Пра̏вило:
  - пра̏вило тѐо̄рија predvidjeti да за an item iff neki od њѐгов conditions matches да item
  - пра̏вило an изузе́так би̏ти an item чѝјӣ предсказање differs из њѐгов налепница
  - пра̏вило zbroj ко̏штати би̏ти тѐо̄рија величѝна plus изузе́так ко̏штати times бро̑ј od exceptions
  - пра̏вило изабрати тѐо̄рија od minimalan zbroj ко̏штати
Mdl optimal тѐо̄рија kakav?

Ehdokkaat:
  - theory2 teoria koko 2
  - theory0 teoria koko 4
  - ennakoida kyllä jos: theory0, muoto ja kartio
  - ennakoida kyllä jos: theory3, muoto ja pallo
  - theory3 teoria koko 4
  - theory1 teoria koko 4
  - ennakoida kyllä jos: theory2, muoto ja kartio
  - ennakoida kyllä jos: theory1, muoto ja kuutio
  - ennakoida kyllä jos: theory1, muoto ja kartio
  - ennakoida kyllä jos: theory3, muoto ja kartio
  - ennakoida kyllä jos: theory0, muoto ja kuutio
Kustannus malli: poikkeus maksaa 2.
Tieto:
  - esine: i7, punainen, kartio ja ei
  - esine: i0, sininen, pallo ja ei
  - esine: i4, punainen, kuutio ja ei
  - esine: i1, vihreä, kartio ja ei
  - esine: i6, punainen, kuutio ja ei
  - esine: i5, vihreä, kartio ja ei
  - esine: i3, punainen, kartio ja kyllä
  - esine: i2, punainen, kartio ja kyllä
Säännöt:
  - sääntö teoria ennakoida kyllä sillä mikäli esine joss jotkut vaille sen conditions matches että esine
  - sääntö mikäli poikkeus tulla mikäli esine kenen ennustus differs from sen etiketti
  - sääntö summa maksaa tulla teoria koko plus poikkeus maksaa kertaa luku vaille exceptions
  - sääntö valita teoria vaille vähin summa maksaa
Mikä mdl optimaalinen teoria?

候选项如下：
  - theory2的理论规模是2
  - theory0的理论规模是4
  - 预测是若：theory0、形状和圆锥
  - 预测是若：theory3、形状和球体
  - theory3的理论规模是4
  - theory1的理论规模是4
  - 预测是若：theory2、形状和圆锥
  - 预测是若：theory1、形状和立方体
  - 预测是若：theory1、形状和圆锥
  - 预测是若：theory3、形状和圆锥
  - 预测是若：theory0、形状和立方体
代价模型如下：exception的成本：2。
数据如下：
  - 的物品是：i7、红色的、圆锥和否
  - 的物品是：i0、蓝色的、球体和否
  - 的物品是：i4、红色的、立方体和否
  - 的物品是：i1、绿色的、圆锥和否
  - 的物品是：i6、红色的、立方体和否
  - 的物品是：i5、绿色的、圆锥和否
  - 的物品是：i3、红色的、圆锥和是
  - 的物品是：i2、红色的、圆锥和是
规则如下：规则：一的理论是预测是foran的物品是当且仅当有的itsconditions匹配that的物品是；规则：anexception是an的物品是whosepredictiondiffersfromitslabel；规则：total的成本是是的理论是大小plusexception的成本是乘以数字的exceptions；规则：choose这的理论是的leasttotal的成本是。
mdloptimal的理论是什么？

候補者:
  - theory22理論大きさ
  - theory04理論大きさ
  - 予言するはいもし...:theory0, 形と松毬
  - 予言するはいもし...:theory3, 形と球
  - theory34理論大きさ
  - theory14理論大きさ
  - 予言するはいもし...:theory2, 形と松毬
  - 予言するはいもし...:theory1, 形と立方体
  - 予言するはいもし...:theory1, 形と松毬
  - 予言するはいもし...:theory3, 形と松毬
  - 予言するはいもし...:theory0, 形と立方体
費用モデル:例外費やす:2.
資料:
  - 項目:i7, 赤い, 松毬といいえ
  - 項目:i0, 青い, 球といいえ
  - 項目:i4, 赤い, 立方体といいえ
  - 項目:i1, 緑の, 松毬といいえ
  - 項目:i6, 赤い, 立方体といいえ
  - 項目:i5, 緑の, 松毬といいえ
  - 項目:i3, 赤い, 松毬とはい
  - 項目:i2, 赤い, 松毬とはい
規則:規則:理論予言するはい...のためan項目iffいくつかの中のitsconditionsmatchesその項目; 規則:an例外れるan項目誰の予言differsからits荷札; 規則:総計費やすれる理論大きさ足す例外費やす掛ける数の中のexceptions; 規則:選ぶ理論の中の最小の総計費やす.
mdl最適な理論何と?

Adaylar:
  - theory2 2 kuram boyut
  - theory0 4 kuram boyut
  - öngörür evet eğer: theory0, şekil ve koni
  - öngörür evet eğer: theory3, şekil ve küre
  - theory3 4 kuram boyut
  - theory1 4 kuram boyut
  - öngörür evet eğer: theory2, şekil ve koni
  - öngörür evet eğer: theory1, şekil ve küp
  - öngörür evet eğer: theory1, şekil ve koni
  - öngörür evet eğer: theory3, şekil ve koni
  - öngörür evet eğer: theory0, şekil ve küp
Cost model: exception maliyet: 2.
Veriler:
  - item: i7, kırmızı, koni ve hayır
  - item: i0, mavi, küre ve hayır
  - item: i4, kırmızı, küp ve hayır
  - item: i1, yeşil, koni ve hayır
  - item: i6, kırmızı, küp ve hayır
  - item: i5, yeşil, koni ve hayır
  - item: i3, kırmızı, koni ve evet
  - item: i2, kırmızı, koni ve evet
Kurallar:
  - kural: bir kuram öngörür evet for an item iff bazı its conditions matches that item
  - kural: an exception an item whose prediction differs from its label
  - kural: total maliyet kuram boyut plus exception maliyet times sayı exceptions
  - kural: choose kuram least total maliyet
Mdl optimal kuram ne?

مُرَشَّح:
  - نَظَرِيَّة مَقَاس theory2 2
  - نَظَرِيَّة مَقَاس theory0 4
  - تَنَبَّأَ نَعَم إِنْ: theory0، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ كُرَة
  - نَظَرِيَّة مَقَاس theory3 4
  - نَظَرِيَّة مَقَاس theory1 4
  - تَنَبَّأَ نَعَم إِنْ: theory2، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ كَعْبَة
  - تَنَبَّأَ نَعَم إِنْ: theory1، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory3، شَكْل وَـ مَخْرُوط
  - تَنَبَّأَ نَعَم إِنْ: theory0، شَكْل وَـ كَعْبَة
cost model: اِسْتِثْنَاء cost 2.
بَيَانَات:
  - بَنْد: i7، أَحْمَر، مَخْرُوط وَـ no
  - بَنْد: i0، أَزْرَق، كُرَة وَـ no
  - بَنْد: i4، أَحْمَر، كَعْبَة وَـ no
  - بَنْد: i1، أَخْضَر، مَخْرُوط وَـ no
  - بَنْد: i6، أَحْمَر، كَعْبَة وَـ no
  - بَنْد: i5، أَخْضَر، مَخْرُوط وَـ no
  - بَنْد: i3، أَحْمَر، مَخْرُوط وَـ نَعَم
  - بَنْد: i2، أَحْمَر، مَخْرُوط وَـ نَعَم
قَانُون:
  - قَانُون نَظَرِيَّة تَنَبَّأَ نَعَم لِأَنَّ an بَنْد iff بَعْضٌ مِنْ its conditions matches أَنْ بَنْد
  - قَانُون an اِسْتِثْنَاء كَانَ an بَنْد لِمَن تَنَبُّؤ differs مِن its مُلْصَق
  - قَانُون مَجْمُوع cost كَانَ نَظَرِيَّة مَقَاس زَائِد اِسْتِثْنَاء cost فِي عَدَد مِنْ exceptions
  - قَانُون اِخْتَارَ الـ نَظَرِيَّة مِنْ أَقَلَّ مَجْمُوع cost
mdl optimal نَظَرِيَّة مَاذَا؟

Კანდიდატები:
  - theory2 2 თეორია ზომა
  - theory0 4 თეორია ზომა
  - predict დიახ თუ: theory0, ფორმა და გირჩა
  - predict დიახ თუ: theory3, ფორმა და სფერო
  - theory3 4 თეორია ზომა
  - theory1 4 თეორია ზომა
  - predict დიახ თუ: theory2, ფორმა და გირჩა
  - predict დიახ თუ: theory1, ფორმა და კუბი
  - predict დიახ თუ: theory1, ფორმა და გირჩა
  - predict დიახ თუ: theory3, ფორმა და გირჩა
  - predict დიახ თუ: theory0, ფორმა და კუბი
Ფასი მოდელი: გამონაკლისი ღირს 2.
Მონაცემები:
  - item: i7, წითელი, გირჩა და no
  - item: i0, ლურჯი, სფერო და no
  - item: i4, წითელი, კუბი და no
  - item: i1, მწვანე, გირჩა და no
  - item: i6, წითელი, კუბი და no
  - item: i5, მწვანე, გირჩა და no
  - item: i3, წითელი, გირჩა და დიახ
  - item: i2, წითელი, გირჩა და დიახ
Წესი:
  - წესი თეორია predict დიახ for ერთ-ერთი item iff some of მისი conditions matches რომ item
  - წესი ერთ-ერთი გამონაკლისი ყოფნა ერთ-ერთი item ვისი წინასწარმეტყველება differs from მისი ეტიკეტი
  - წესი total ღირს ყოფნა თეორია ზომა plus გამონაკლისი ღირს times რიცხვი of exceptions
  - წესი ამორჩევა თეორია of least total ღირს
Mdl optimal თეორია რა?

Kaiwhakauru:
  - ariā size theory2 2
  - ariā size theory0 4
  - matakite āe mēnā: theory0, āhuahanga me koeko
  - matakite āe mēnā: theory3, āhuahanga me poi
  - ariā size theory3 4
  - ariā size theory1 4
  - matakite āe mēnā: theory2, āhuahanga me koeko
  - matakite āe mēnā: theory1, āhuahanga me mataono
  - matakite āe mēnā: theory1, āhuahanga me koeko
  - matakite āe mēnā: theory3, āhuahanga me koeko
  - matakite āe mēnā: theory0, āhuahanga me mataono
Cost model: hāungatanga cost 2.
Hōtuku:
  - tūemi: i7, red, koeko me kāo
  - tūemi: i0, kahurangi, poi me kāo
  - tūemi: i4, red, mataono me kāo
  - tūemi: i1, kākāriki, koeko me kāo
  - tūemi: i6, red, mataono me kāo
  - tūemi: i5, kākāriki, koeko me kāo
  - tūemi: i3, red, koeko me āe
  - tūemi: i2, red, koeko me āe
Rekureihana:
  - rekureihana ariā matakite āe mo he tūemi iff ētahi o its conditions matches tēnā tūemi
  - rekureihana he hāungatanga is he tūemi whose prediction differs from its tapanga
  - rekureihana tapeke cost is ariā size ngahoro hāungatanga cost times tokopae o exceptions
  - rekureihana whiri te ariā o least tapeke cost
Mdl optimal ariā unutai?

Candidates:
  - theory2 lý thuyết kích thước 2
  - theory0 lý thuyết kích thước 4
  - dự báo vâng nếu: theory0, hình dáng và nón
  - dự báo vâng nếu: theory3, hình dáng và mặt cầu
  - theory3 lý thuyết kích thước 4
  - theory1 lý thuyết kích thước 4
  - dự báo vâng nếu: theory2, hình dáng và nón
  - dự báo vâng nếu: theory1, hình dáng và lập phương
  - dự báo vâng nếu: theory1, hình dáng và nón
  - dự báo vâng nếu: theory3, hình dáng và nón
  - dự báo vâng nếu: theory0, hình dáng và lập phương
Cost model: lệ ngoại cost 2.
Dữ liệu:
  - vật phẩm: i7, đỏ, nón và no
  - vật phẩm: i0, blue, mặt cầu và no
  - vật phẩm: i4, đỏ, lập phương và no
  - vật phẩm: i1, green, nón và no
  - vật phẩm: i6, đỏ, lập phương và no
  - vật phẩm: i5, green, nón và no
  - vật phẩm: i3, đỏ, nón và vâng
  - vật phẩm: i2, đỏ, nón và vâng
Quy tắc:
  - quy tắc lý thuyết dự báo vâng cho an vật phẩm iff vài bằng its conditions matches rằng vật phẩm
  - quy tắc an lệ ngoại được an vật phẩm của ai prediction differs từ its nhãn
  - quy tắc tổng cost được lý thuyết kích thước cộng lệ ngoại cost nhân số bằng exceptions
  - quy tắc chọn the lý thuyết bằng least tổng cost
Mdl optimal lý thuyết gì?

ผู้สมัคร:
  - theory2ทฤษฎีขนาด2
  - theory0ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory0, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและทรงกลม
  - theory3ทฤษฎีขนาด4
  - theory1ทฤษฎีขนาด4
  - ทำนายใช่ถ้า หาก:theory2, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและลูกบาศก์
  - ทำนายใช่ถ้า หาก:theory1, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory3, รูปร่างและกรวย
  - ทำนายใช่ถ้า หาก:theory0, รูปร่างและลูกบาศก์
ค่าใชีข่ายนางแบบ:ข้อยกเว้นราคา:2.
ข้อมูล:
  - item:i7, แดง, กรวยและไฮ้
  - item:i0, น้ำเงิน, ทรงกลมและไฮ้
  - item:i4, แดง, ลูกบาศก์และไฮ้
  - item:i1, เขียว, กรวยและไฮ้
  - item:i6, แดง, ลูกบาศก์และไฮ้
  - item:i5, เขียว, กรวยและไฮ้
  - item:i3, แดง, กรวยและใช่
  - item:i2, แดง, กรวยและใช่
กฎ:
  - กฎ:ทฤษฎีทำนายใช่เพื่อหนึ่งitemiffบางของitsconditionsmatchesว่าitem
  - กฎ:หนึ่งข้อยกเว้นถูกหนึ่งitemwhosepredictiondiffersจากitsฉลาก
  - กฎ:totalราคาถูกทฤษฎีขนาดบวกข้อยกเว้นราคาคูณจำนวนของexceptions
  - กฎ:เลือกทฤษฎีของleasttotalราคา
mdloptimalทฤษฎีอะไร?

வேட்பாளர்:
  - theory2 2 theory size
  - theory0 4 theory size
  - கணி ஆம் என்றால்: theory0, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory3, shape மற்றும் sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - கணி ஆம் என்றால்: theory2, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cube
  - கணி ஆம் என்றால்: theory1, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory3, shape மற்றும் cone
  - கணி ஆம் என்றால்: theory0, shape மற்றும் cube
cost model: exception செலவு 2.
தரவுகள்:
  - item: i7, சிவப்பு, cone மற்றும் no
  - item: i0, நீலம், sphere மற்றும் no
  - item: i4, சிவப்பு, cube மற்றும் no
  - item: i1, பச்சை, cone மற்றும் no
  - item: i6, சிவப்பு, cube மற்றும் no
  - item: i5, பச்சை, cone மற்றும் no
  - item: i3, சிவப்பு, cone மற்றும் ஆம்
  - item: i2, சிவப்பு, cone மற்றும் ஆம்
rules:
  - rule theory கணி ஆம் for ஒரு item iff சில கொண்ட அதன் conditions matches எப்படி item
  - rule ஒரு exception படு ஒரு item யாருடைய prediction differs இருந்து அதன் label
  - rule மொத்தம் செலவு படு theory size plus exception செலவு times எண் கொண்ட exceptions
  - rule choose theory கொண்ட least மொத்தம் செலவு
mdl optimal theory என்ன?

Wagombea ni:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ndiyo kama: theory0, umbo na koni
  - predict ndiyo kama: theory3, umbo na tufe
  - theory3 theory size 4
  - theory1 theory size 4
  - predict ndiyo kama: theory2, umbo na koni
  - predict ndiyo kama: theory1, umbo na mchemraba
  - predict ndiyo kama: theory1, umbo na koni
  - predict ndiyo kama: theory3, umbo na koni
  - predict ndiyo kama: theory0, umbo na mchemraba
Cost model: exception gharimu: 2.
Data:
  - item: i7, ekundu, koni na hapana
  - item: i0, buluu, tufe na hapana
  - item: i4, ekundu, mchemraba na hapana
  - item: i1, kijani, koni na hapana
  - item: i6, ekundu, mchemraba na hapana
  - item: i5, kijani, koni na hapana
  - item: i3, ekundu, koni na ndiyo
  - item: i2, ekundu, koni na ndiyo
Sheria ni:
  - sheria: theory predict ndiyo for an item iff baadhi ya its conditions matches that item
  - sheria: an exception ni an item whose prediction differs from its label
  - sheria: total gharimu ni theory size plus exception gharimu times namba ya exceptions
  - sheria: choose theory ya least total gharimu
Mdl optimal theory nini?

Candidates:
  - theory2 2 theory size
  - theory0 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory0, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - predict aooʼ ládą́ą́ʼ: theory2, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cube
  - predict aooʼ ládą́ą́ʼ: theory1, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory3, shape dóó cone
  - predict aooʼ ládą́ą́ʼ: theory0, shape dóó cube
Cost model: exception bą́ą́h ílį́ 2.
Data:
  - item: i7, łichííʼ, cone dóó no
  - item: i0, dootłʼizh, sphere dóó no
  - item: i4, łichííʼ, cube dóó no
  - item: i1, dinootłʼizh, cone dóó no
  - item: i6, łichííʼ, cube dóó no
  - item: i5, dinootłʼizh, cone dóó no
  - item: i3, łichííʼ, cone dóó aooʼ
  - item: i2, łichííʼ, cone dóó aooʼ
Bee hazʼáanii:
  - bee hazʼáanii theory predict aooʼ for an item iff łaʼ of its conditions matches éí item
  - bee hazʼáanii an exception átʼé an item whose prediction differs from its label
  - bee hazʼáanii total bą́ą́h ílį́ átʼé theory size plus exception bą́ą́h ílį́ ahą́ą́h nániilyá námboo of exceptions
  - bee hazʼáanii choose theory of least total bą́ą́h ílį́
Mdl optimal theory haʼátʼíísh?

Candidates:
  - theory2 2 theory size
  - theory0 4 theory size
  - predict aap if: theory0, shape aamma cone
  - predict aap if: theory3, shape aamma sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - predict aap if: theory2, shape aamma cone
  - predict aap if: theory1, shape aamma cube
  - predict aap if: theory1, shape aamma cone
  - predict aap if: theory3, shape aamma cone
  - predict aap if: theory0, shape aamma cube
Cost model: exception cost 2.
Data:
  - item: i7, aappalajaarpoq, cone aamma no
  - item: i0, tungujortoq, sphere aamma no
  - item: i4, aappalajaarpoq, cube aamma no
  - item: i1, qorsuk, cone aamma no
  - item: i6, aappalajaarpoq, cube aamma no
  - item: i5, qorsuk, cone aamma no
  - item: i3, aappalajaarpoq, cone aamma aap
  - item: i2, aappalajaarpoq, cone aamma aap
Rules:
  - rule theory predict aap for an item iff some of uuma conditions matches that item
  - rule an exception a an item whose prediction differs from uuma label
  - rule total cost a theory size plus exception cost times kisitsit of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ya sapos: theory0, shape na cone
  - predict ya sapos: theory3, shape na sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict ya sapos: theory2, shape na cone
  - predict ya sapos: theory1, shape na cube
  - predict ya sapos: theory1, shape na cone
  - predict ya sapos: theory3, shape na cone
  - predict ya sapos: theory0, shape na cube
Cost model: exception cost 2.
Data:
  - item: i7, ret, cone na no
  - item: i0, blu, sphere na no
  - item: i4, ret, cube na no
  - item: i1, grinpela, cone na no
  - item: i6, ret, cube na no
  - item: i5, grinpela, cone na no
  - item: i3, ret, cone na ya
  - item: i2, ret, cone na ya
Rules:
  - rule theory predict ya for wanpela item iff sampela bilong its conditions matches that item
  - rule wanpela exception is wanpela item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number bilong exceptions
  - rule choose theory bilong least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory3, shape sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes if: theory2, shape cone
  - predict yes if: theory1, shape cube
  - predict yes if: theory1, shape cone
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, màxke, cone no
  - item: i0, blue, sphere no
  - item: i4, màxke, cube no
  - item: i1, green, cone no
  - item: i6, màxke, cube no
  - item: i5, green, cone no
  - item: i3, màxke, cone yes
  - item: i2, màxke, cone yes
Nikanixi:
  - nikanixi theory predict yes for an item iff some of its conditions matches that item
  - nikanixi an exception is an item whose prediction differs from its label
  - nikanixi total cost is theory size plus exception cost times number of exceptions
  - nikanixi choose theory of least total cost
Mdl optimal theory kwèch?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, shape а cone
  - predict yes if: theory3, shape а sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes if: theory2, shape а cone
  - predict yes if: theory1, shape а cube
  - predict yes if: theory1, shape а cone
  - predict yes if: theory3, shape а cone
  - predict yes if: theory0, shape а cube
Cost model: exception cost 2.
Data:
  - item: i7, цӏе, cone а no
  - item: i0, blue, sphere а no
  - item: i4, цӏе, cube а no
  - item: i1, баьццара, cone а no
  - item: i6, цӏе, cube а no
  - item: i5, баьццара, cone а no
  - item: i3, цӏе, cone а yes
  - item: i2, цӏе, cone а yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception хила an item whose prediction differs from its label
  - rule total cost хила theory size plus exception cost times number of exceptions
  - rule хержа theory of least total cost
Mdl optimal theory фу?

후보자:
  - theory2 2 이론 크기
  - theory0 4 이론 크기
  - 예언하다 네 만약: theory0, 모양 솔방울
  - 예언하다 네 만약: theory3, 모양 구체
  - theory3 4 이론 크기
  - theory1 4 이론 크기
  - 예언하다 네 만약: theory2, 모양 솔방울
  - 예언하다 네 만약: theory1, 모양 정육면체
  - 예언하다 네 만약: theory1, 모양 솔방울
  - 예언하다 네 만약: theory3, 모양 솔방울
  - 예언하다 네 만약: theory0, 모양 정육면체
cost model: 예외 cost 2.
자료:
  - 아이템: i7, 빨갛다, 솔방울 아니요
  - 아이템: i0, 푸르다, 구체 아니요
  - 아이템: i4, 빨갛다, 정육면체 아니요
  - 아이템: i1, 녹색의, 솔방울 아니요
  - 아이템: i6, 빨갛다, 정육면체 아니요
  - 아이템: i5, 녹색의, 솔방울 아니요
  - 아이템: i3, 빨갛다, 솔방울 네
  - 아이템: i2, 빨갛다, 솔방울 네
규칙:
  - 규칙 이론 예언하다 네 ...때문에 an 아이템 iff 어떤 ...의 its conditions matches 그 아이템
  - 규칙 an 예외 is an 아이템 누구의 예언 differs 부터 its 라벨
  - 규칙 합계 cost is 이론 크기 더하기 예외 cost 곱하기 숫자 ...의 exceptions
  - 규칙 고르다 이론 ...의 가장 적게 합계 cost
mdl optimal 이론 무엇?

Kandidatu:
  - theory2 2 theory tamaina
  - theory0 4 theory tamaina
  - aurresan bai if: theory0, shape eta pinaburu
  - aurresan bai if: theory3, shape eta esfera
  - theory3 4 theory tamaina
  - theory1 4 theory tamaina
  - aurresan bai if: theory2, shape eta pinaburu
  - aurresan bai if: theory1, shape eta kubo
  - aurresan bai if: theory1, shape eta pinaburu
  - aurresan bai if: theory3, shape eta pinaburu
  - aurresan bai if: theory0, shape eta kubo
Cost model: exception kostatu 2.
Data:
  - item: i7, gorri, pinaburu eta no
  - item: i0, urdin, esfera eta no
  - item: i4, gorri, kubo eta no
  - item: i1, berde, pinaburu eta no
  - item: i6, gorri, kubo eta no
  - item: i5, berde, pinaburu eta no
  - item: i3, gorri, pinaburu eta bai
  - item: i2, gorri, pinaburu eta bai
Arau:
  - arau bat theory aurresan bai for bat item iff some of its conditions matches hura item
  - arau bat exception izan bat item whose prediction differs from its label
  - arau total kostatu izan theory tamaina gehi exception kostatu bider zenbaki of exceptions
  - arau aukeratu the theory of least total kostatu
Mdl optimal theory zer?

ᎠᏙᎩᏯᏍᎩ:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ᎥᎥ if: theory0, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory3, shape ᎠᎴ sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict ᎥᎥ if: theory2, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cube
  - predict ᎥᎥ if: theory1, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory3, shape ᎠᎴ cone
  - predict ᎥᎥ if: theory0, shape ᎠᎴ cube
Cost model: exception cost 2.
Data:
  - item: i7, ᎩᎦᎨ, cone ᎠᎴ ᏝᏃ
  - item: i0, ᏌᎪᏂᎨᎢ, sphere ᎠᎴ ᏝᏃ
  - item: i4, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
  - item: i1, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᏝᏃ
  - item: i6, ᎩᎦᎨ, cube ᎠᎴ ᏝᏃ
  - item: i5, ᎢᏤ ᎢᏳᏍᏗ, cone ᎠᎴ ᏝᏃ
  - item: i3, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
  - item: i2, ᎩᎦᎨ, cone ᎠᎴ ᎥᎥ
Rules:
  - rule theory predict ᎥᎥ for an item iff ᎢᎦᏓ of its conditions matches Ꮎ item
  - rule an exception is an item whose prediction differs ᏂᏛᎴᏅᏓ its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
What mdl optimal theory?

Candidates:
  - theory2 theory chhika 2
  - theory0 theory chhika 4
  - predict arí if: theory0, shape cone
  - predict arí if: theory3, shape sphere
  - theory3 theory chhika 4
  - theory1 theory chhika 4
  - predict arí if: theory2, shape cone
  - predict arí if: theory1, shape cube
  - predict arí if: theory1, shape cone
  - predict arí if: theory3, shape cone
  - predict arí if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, puka, cone mánan
  - item: i0, anqas, sphere mánan
  - item: i4, puka, cube mánan
  - item: i1, waylla, cone mánan
  - item: i6, puka, cube mánan
  - item: i5, waylla, cone mánan
  - item: i3, puka, cone arí
  - item: i2, puka, cone arí
Rules:
  - rule theory predict arí for an item iff some of its conditions matches chay item
  - rule an exception tiyay an item whose prediction differs from its label
  - rule total cost tiyay theory chhika plus exception cost times yupay of exceptions
  - rule akllay theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 тиориэ size
  - theory0 4 тиориэ size
  - predict yes if: theory0, shape ыкӏи cone
  - predict yes if: theory3, shape ыкӏи sphere
  - theory3 4 тиориэ size
  - theory1 4 тиориэ size
  - predict yes if: theory2, shape ыкӏи cone
  - predict yes if: theory1, shape ыкӏи cube
  - predict yes if: theory1, shape ыкӏи cone
  - predict yes if: theory3, shape ыкӏи cone
  - predict yes if: theory0, shape ыкӏи cube
Cost model: exception cost 2.
Data:
  - item: i7, плъыжьы, cone ыкӏи no
  - item: i0, blue, sphere ыкӏи no
  - item: i4, плъыжьы, cube ыкӏи no
  - item: i1, уцышъо, cone ыкӏи no
  - item: i6, плъыжьы, cube ыкӏи no
  - item: i5, уцышъо, cone ыкӏи no
  - item: i3, плъыжьы, cone ыкӏи yes
  - item: i2, плъыжьы, cone ыкӏи yes
Rules:
  - rule тиориэ predict yes for an item iff горэ of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is тиориэ size plus exception cost times number of exceptions
  - rule choose the тиориэ of least total cost
Mdl optimal тиориэ сыд?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, shape thiab cone
  - predict yes if: theory3, shape thiab sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes if: theory2, shape thiab cone
  - predict yes if: theory1, shape thiab cube
  - predict yes if: theory1, shape thiab cone
  - predict yes if: theory3, shape thiab cone
  - predict yes if: theory0, shape thiab cube
Cost model: exception nqi 2.
Data:
  - item: i7, liab, cone thiab no
  - item: i0, xiav, sphere thiab no
  - item: i4, liab, cube thiab no
  - item: i1, ntsuab, cone thiab no
  - item: i6, liab, cube thiab no
  - item: i5, ntsuab, cone thiab no
  - item: i3, liab, cone thiab yes
  - item: i2, liab, cone thiab yes
Cai:
  - cai theory predict yes for an item iff some of its conditions matches qhov ntawd item
  - cai an exception is an item whose prediction differs from its label
  - cai total nqi is theory size plus exception nqi times number of exceptions
  - cai choose theory of least total nqi
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ngai if: theory0, shape cone
  - predict ngai if: theory3, shape sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict ngai if: theory2, shape cone
  - predict ngai if: theory1, shape cube
  - predict ngai if: theory1, shape cone
  - predict ngai if: theory3, shape cone
  - predict ngai if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone woi
  - item: i0, blue, sphere woi
  - item: i4, red, cube woi
  - item: i1, green, cone woi
  - item: i6, red, cube woi
  - item: i5, green, cone woi
  - item: i3, red, cone ngai
  - item: i2, red, cone ngai
Rules:
  - rule theory predict ngai for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of wy krook total cost
Mdl optimal theory ining-jak?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict i̠i if: theory0, shape micha' cone
  - predict i̠i if: theory3, shape micha' sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict i̠i if: theory2, shape micha' cone
  - predict i̠i if: theory1, shape micha' cube
  - predict i̠i if: theory1, shape micha' cone
  - predict i̠i if: theory3, shape micha' cone
  - predict i̠i if: theory0, shape micha' cube
Cost model: exception cost 2.
Data:
  - item: i7, homa, cone micha' no
  - item: i0, blue, sphere micha' no
  - item: i4, homa, cube micha' no
  - item: i1, green, cone micha' no
  - item: i6, homa, cube micha' no
  - item: i5, green, cone micha' no
  - item: i3, homa, cone micha' i̠i
  - item: i2, homa, cone micha' i̠i
Rules:
  - rule theory predict i̠i for an item iff some of its conditions matches that item
  - rule an exception is an item kata prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory nanta?

Candidates:
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, shape カ cone
  - predict yes if: theory3, shape カ sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - predict yes if: theory2, shape カ cone
  - predict yes if: theory1, shape カ cube
  - predict yes if: theory1, shape カ cone
  - predict yes if: theory3, shape カ cone
  - predict yes if: theory0, shape カ cube
Cost model: exception cost 2.
Data:
  - item: i7, フレ, cone カ ソモ
  - item: i0, blue, sphere カ ソモ
  - item: i4, フレ, cube カ ソモ
  - item: i1, green, cone カ ソモ
  - item: i6, フレ, cube カ ソモ
  - item: i5, green, cone カ ソモ
  - item: i3, フレ, cone カ yes
  - item: i2, フレ, cone カ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs wa its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes эвыр: theory0, shape ынкъам cone
  - predict yes эвыр: theory3, shape ынкъам sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes эвыр: theory2, shape ынкъам cone
  - predict yes эвыр: theory1, shape ынкъам cube
  - predict yes эвыр: theory1, shape ынкъам cone
  - predict yes эвыр: theory3, shape ынкъам cone
  - predict yes эвыр: theory0, shape ынкъам cube
Cost model: exception cost 2.
Data:
  - item: i7, чеԓгыԓьын, cone ынкъам no
  - item: i0, ныԓиԓиԓӄин, sphere ынкъам no
  - item: i4, чеԓгыԓьын, cube ынкъам no
  - item: i1, green, cone ынкъам no
  - item: i6, чеԓгыԓьын, cube ынкъам no
  - item: i5, green, cone ынкъам no
  - item: i3, чеԓгыԓьын, cone ынкъам yes
  - item: i2, чеԓгыԓьын, cone ынкъам yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes wa: theory0, shape yéetel cone
  - predict yes wa: theory3, shape yéetel sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes wa: theory2, shape yéetel cone
  - predict yes wa: theory1, shape yéetel cube
  - predict yes wa: theory1, shape yéetel cone
  - predict yes wa: theory3, shape yéetel cone
  - predict yes wa: theory0, shape yéetel cube
Cost model: exception cost 2.
Data:
  - item: i7, chak, cone yéetel no
  - item: i0, blue, sphere yéetel no
  - item: i4, chak, cube yéetel no
  - item: i1, yaʼax, cone yéetel no
  - item: i6, chak, cube yéetel no
  - item: i5, yaʼax, cone yéetel no
  - item: i3, chak, cone yéetel yes
  - item: i2, chak, cone yéetel yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception yan an item whose prediction differs from its label
  - rule total cost yan theory size plus exception cost times number of exceptions
  - rule yéey theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict hą if: theory0, shape cone
  - predict hą if: theory3, shape sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict hą if: theory2, shape cone
  - predict hą if: theory1, shape cube
  - predict hą if: theory1, shape cone
  - predict hą if: theory3, shape cone
  - predict hą if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone no
  - item: i0, blue, sphere no
  - item: i4, red, cube no
  - item: i1, green, cone no
  - item: i6, red, cube no
  - item: i5, green, cone no
  - item: i3, red, cone hą
  - item: i2, red, cone hą
Rules:
  - rule theory predict hą for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory híli?

Candidates:
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, shape na cone
  - predict yes if: theory3, shape na sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - predict yes if: theory2, shape na cone
  - predict yes if: theory1, shape na cube
  - predict yes if: theory1, shape na cone
  - predict yes if: theory3, shape na cone
  - predict yes if: theory0, shape na cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone na no
  - item: i0, blue, sphere na no
  - item: i4, red, cube na no
  - item: i1, green, cone na no
  - item: i6, red, cube na no
  - item: i5, green, cone na no
  - item: i3, red, cone na yes
  - item: i2, red, cone na yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict heẽ if: theory0, shape cone
  - predict heẽ if: theory3, shape sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict heẽ if: theory2, shape cone
  - predict heẽ if: theory1, shape cube
  - predict heẽ if: theory1, shape cone
  - predict heẽ if: theory3, shape cone
  - predict heẽ if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, pytã, cone nahániri
  - item: i0, blue, sphere nahániri
  - item: i4, pytã, cube nahániri
  - item: i1, green, cone nahániri
  - item: i6, pytã, cube nahániri
  - item: i5, green, cone nahániri
  - item: i3, pytã, cone heẽ
  - item: i2, pytã, cone heẽ
Rules:
  - rule theory predict heẽ for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - theory0 4 theory size
  - predict jisa if: theory0, shape cone
  - predict jisa if: theory3, shape sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - predict jisa if: theory2, shape cone
  - predict jisa if: theory1, shape cube
  - predict jisa if: theory1, shape cone
  - predict jisa if: theory3, shape cone
  - predict jisa if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, chupika, cone no
  - item: i0, larama, sphere no
  - item: i4, chupika, cube no
  - item: i1, ch’uxña, cone no
  - item: i6, chupika, cube no
  - item: i5, ch’uxña, cone no
  - item: i3, chupika, cone jisa
  - item: i2, chupika, cone jisa
Rules:
  - rule maya theory predict jisa for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory ʻǁnàhã 2
  - theory0 theory ʻǁnàhã 4
  - predict ʻée if: theory0, shape ǂʻá cone
  - predict ʻée if: theory3, shape ǂʻá sphere
  - theory3 theory ʻǁnàhã 4
  - theory1 theory ʻǁnàhã 4
  - predict ʻée if: theory2, shape ǂʻá cone
  - predict ʻée if: theory1, shape ǂʻá cube
  - predict ʻée if: theory1, shape ǂʻá cone
  - predict ʻée if: theory3, shape ǂʻá cone
  - predict ʻée if: theory0, shape ǂʻá cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone ǂʻá ʻĩĩ
  - item: i0, blue, sphere ǂʻá ʻĩĩ
  - item: i4, red, cube ǂʻá ʻĩĩ
  - item: i1, green, cone ǂʻá ʻĩĩ
  - item: i6, red, cube ǂʻá ʻĩĩ
  - item: i5, green, cone ǂʻá ʻĩĩ
  - item: i3, red, cone ǂʻá ʻée
  - item: i2, red, cone ǂʻá ʻée
Rules:
  - rule theory predict ʻée for an item iff some of its conditions matches tVʻVV-sà kV item
  - rule an exception ǀîi an item whose prediction differs from its label
  - rule total cost ǀîi theory ʻǁnàhã plus exception cost times ǃɢa̰n-ǃɢa̰n of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, shape cone
  - predict yes if: theory3, shape sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - predict yes if: theory2, shape cone
  - predict yes if: theory1, shape cube
  - predict yes if: theory1, shape cone
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone no
  - item: i0, blue, sphere no
  - item: i4, red, cube no
  - item: i1, green, cone no
  - item: i6, red, cube no
  - item: i5, green, cone no
  - item: i3, red, cone yes
  - item: i2, red, cone yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory3, shape sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes if: theory2, shape cone
  - predict yes if: theory1, shape cube
  - predict yes if: theory1, shape cone
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone no
  - item: i0, blue, sphere no
  - item: i4, red, cube no
  - item: i1, green, cone no
  - item: i6, red, cube no
  - item: i5, green, cone no
  - item: i3, red, cone yes
  - item: i2, red, cone yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 2 theory size
  - theory0 4 theory size
  - predict yes if: theory0, shape cone
  - predict yes if: theory3, shape sphere
  - theory3 4 theory size
  - theory1 4 theory size
  - predict yes if: theory2, shape cone
  - predict yes if: theory1, shape cube
  - predict yes if: theory1, shape cone
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone no
  - item: i0, blue, sphere no
  - item: i4, red, cube no
  - item: i1, green, cone no
  - item: i6, red, cube no
  - item: i5, green, cone no
  - item: i3, red, cone yes
  - item: i2, red, cone yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict ee if: theory0, shape cone
  - predict ee if: theory3, shape sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict ee if: theory2, shape cone
  - predict ee if: theory1, shape cube
  - predict ee if: theory1, shape cone
  - predict ee if: theory3, shape cone
  - predict ee if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone no
  - item: i0, blue, sphere no
  - item: i4, red, cube no
  - item: i1, green, cone no
  - item: i6, red, cube no
  - item: i5, green, cone no
  - item: i3, red, cone ee
  - item: i2, red, cone ee
Rules:
  - rule theory predict ee for an item iff some of its conditions matches that item
  - rule an exception is an item whose prediction differs from its label
  - rule total cost is theory size plus exception cost times number of exceptions
  - rule choose the theory of least total cost
Mdl optimal theory what?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, shape cone
  - predict yes if: theory3, shape sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes if: theory2, shape cone
  - predict yes if: theory1, shape cube
  - predict yes if: theory1, shape cone
  - predict yes if: theory3, shape cone
  - predict yes if: theory0, shape cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone inte
  - item: i0, blue, sphere inte
  - item: i4, red, cube inte
  - item: i1, green, cone inte
  - item: i6, red, cube inte
  - item: i5, green, cone inte
  - item: i3, red, cone yes
  - item: i2, red, cone yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception nemi an item whose prediction differs from its label
  - rule total cost nemi theory size plus exception cost times number of exceptions
  - rule choose ne theory of least total cost
Mdl optimal theory tay?

Les candidats sont:
  - theory2 théorie taille 2
  - theory0 théorie taille 4
  - prédire oui si: theory0, forme et cône
  - prédire oui si: theory3, forme et sphère
  - theory3 théorie taille 4
  - theory1 théorie taille 4
  - prédire oui si: theory2, forme et cône
  - prédire oui si: theory1, forme et cube
  - prédire oui si: theory1, forme et cône
  - prédire oui si: theory3, forme et cône
  - prédire oui si: theory0, forme et cube
Coûts : exception coûter 2.
Les données sont:
  - article: i7, rouge, cône et no
  - article: i0, bleu, sphère et no
  - article: i4, rouge, cube et no
  - article: i1, vert, cône et no
  - article: i6, rouge, cube et no
  - article: i5, vert, cône et no
  - article: i3, rouge, cône et oui
  - article: i2, rouge, cône et oui
Les règles sont:
  - règle un théorie prédire oui parce qu'un article ssi quelque de son conditions matches qu'article
  - règle un exception être un article à qui prédiction differs de son étiquette
  - règle total coûter être théorie taille plus exception coûter fois nombre d'exceptions
  - règle choisir le théorie du moindre total coûter
Quoi mdl optimal théorie ?

Kandidato:
  - theory2 teorio grandeco 2
  - theory0 teorio grandeco 4
  - predict jes se: theory0, formi kaj strobilo
  - predict jes se: theory3, formi kaj sfero
  - theory3 teorio grandeco 4
  - theory1 teorio grandeco 4
  - predict jes se: theory2, formi kaj strobilo
  - predict jes se: theory1, formi kaj kubo
  - predict jes se: theory1, formi kaj strobilo
  - predict jes se: theory3, formi kaj strobilo
  - predict jes se: theory0, formi kaj kubo
Kosto pozistino: escepto kosti 2.
Datumo:
  - ero: i7, ruĝa, strobilo kaj no
  - ero: i0, blua, sfero kaj no
  - ero: i4, ruĝa, kubo kaj no
  - ero: i1, verda, strobilo kaj no
  - ero: i6, ruĝa, kubo kaj no
  - ero: i5, verda, strobilo kaj no
  - ero: i3, ruĝa, strobilo kaj jes
  - ero: i2, ruĝa, strobilo kaj jes
Regulo:
  - regulo teorio predict jes ĉar se ero iff iu de ĝia conditions matches ke ero
  - regulo se escepto egali se ero kies prognozo differs el ĝia etikedo
  - regulo totalo kosti egali teorio grandeco plus escepto kosti times nombro de exceptions
  - regulo elekti teorio de plej malmulta totalo kosti
Mdl optimal teorio kio?

Indulók:
  - theory2 elmélet méret 2
  - theory0 elmélet méret 4
  - megjósol igen ha: theory0, alak és toboz
  - megjósol igen ha: theory3, alak és gömb
  - theory3 elmélet méret 4
  - theory1 elmélet méret 4
  - megjósol igen ha: theory2, alak és toboz
  - megjósol igen ha: theory1, alak és kocka
  - megjósol igen ha: theory1, alak és toboz
  - megjósol igen ha: theory3, alak és toboz
  - megjósol igen ha: theory0, alak és kocka
Költség manöken: kivétel kerül 2.
Adat:
  - cikk: i7, piros, toboz és no
  - cikk: i0, kék, gömb és no
  - cikk: i4, piros, kocka és no
  - cikk: i1, zöld, toboz és no
  - cikk: i6, piros, kocka és no
  - cikk: i5, zöld, toboz és no
  - cikk: i3, piros, toboz és igen
  - cikk: i2, piros, toboz és igen
Szabályok:
  - szabály elmélet megjósol igen mert egy cikk acsa egyesek of övé conditions matches hogy cikk
  - szabály egy kivétel van egy cikk kié? jóslat differs from övé címke
  - szabály végösszeg kerül van elmélet méret meg kivétel kerül times szám of exceptions
  - szabály kiválaszt a elmélet of legkisebb végösszeg kerül
Mdl optimális elmélet melyik?

候選人:
  - theory2theory size2
  - theory0theory size4
  - predict係要是:theory0, 形狀及cone
  - predict係要是:theory3, 形狀及sphere
  - theory3theory size4
  - theory1theory size4
  - predict係要是:theory2, 形狀及cone
  - predict係要是:theory1, 形狀及cube
  - predict係要是:theory1, 形狀及cone
  - predict係要是:theory3, 形狀及cone
  - predict係要是:theory0, 形狀及cube
cost model:exception cost:2.
數據:
  - item:i7, 紅, cone及no
  - item:i0, 藍色嘅, sphere及no
  - item:i4, 紅, cube及no
  - item:i1, 綠色嘅, cone及no
  - item:i6, 紅, cube及no
  - item:i5, 綠色嘅, cone及no
  - item:i3, 紅, cone及係
  - item:i2, 紅, cone及係
rules:rule:一theorypredict係foranitemiff有啲...嘅itsconditionsmatches嘅item; rule:anexception是anitemwhosepredictiondiffers從itslabel; rule:totalcost是theorysize加exceptioncosttimes號碼...嘅exceptions; rule:揀theory...嘅leasttotalcost.
mdl optimal theory乜嘢?

Candidates:
  - theory2 theory size 2
  - theory0 theory size 4
  - predict yes if: theory0, shape とぅ cone
  - predict yes if: theory3, shape とぅ sphere
  - theory3 theory size 4
  - theory1 theory size 4
  - predict yes if: theory2, shape とぅ cone
  - predict yes if: theory1, shape とぅ cube
  - predict yes if: theory1, shape とぅ cone
  - predict yes if: theory3, shape とぅ cone
  - predict yes if: theory0, shape とぅ cube
Cost model: exception cost 2.
Data:
  - item: i7, red, cone とぅ no
  - item: i0, blue, sphere とぅ no
  - item: i4, red, cube とぅ no
  - item: i1, green, cone とぅ no
  - item: i6, red, cube とぅ no
  - item: i5, green, cone とぅ no
  - item: i3, red, cone とぅ yes
  - item: i2, red, cone とぅ yes
Rules:
  - rule theory predict yes for an item iff some of its conditions matches that item
  - rule an exception 有ん an item whose prediction differs from its label
  - rule total cost 有ん theory size plus exception cost times number of exceptions
  - rule choose theory of least total cost
Mdl optimal theory what?

Үміткер:
  - theory2 теория мөлшер 2
  - theory0 теория мөлшер 4
  - predict иә егер: theory0, нысан және бүршік
  - predict иә егер: theory3, нысан және сфера
  - theory3 теория мөлшер 4
  - theory1 теория мөлшер 4
  - predict иә егер: theory2, нысан және бүршік
  - predict иә егер: theory1, нысан және куб
  - predict иә егер: theory1, нысан және бүршік
  - predict иә егер: theory3, нысан және бүршік
  - predict иә егер: theory0, нысан және куб
Тұру үлгі: ерекшелік тұру 2.
Деректер:
  - зат: i7, қызыл, бүршік және жоқ
  - зат: i0, көк, сфера және жоқ
  - зат: i4, қызыл, куб және жоқ
  - зат: i1, жасыл, бүршік және жоқ
  - зат: i6, қызыл, куб және жоқ
  - зат: i5, жасыл, бүршік және жоқ
  - зат: i3, қызыл, бүршік және иә
  - зат: i2, қызыл, бүршік және иә
Ереже:
  - ереже теория predict иә for an зат iff some туралы its conditions matches that зат
  - ереже an ерекшелік is an зат كىمنىڭ сәуегейлік differs from its құлақша
  - ереже total тұру is теория мөлшер plus ерекшелік тұру times сан туралы exceptions
  - ереже таңдау теория туралы least total тұру
Mdl оңтайлы теория не?

מועמדת:
  - theory2 תּוֹרָה גֹּדֶל 2
  - theory0 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory0, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory3, צוּרָה וְ־ כַּדּוּר
  - theory3 תּוֹרָה גֹּדֶל 4
  - theory1 תּוֹרָה גֹּדֶל 4
  - predict כֵּן אִם: theory2, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory1, צוּרָה וְ־ קובייה
  - predict כֵּן אִם: theory1, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory3, צוּרָה וְ־ אצטרובל
  - predict כֵּן אִם: theory0, צוּרָה וְ־ קובייה
עלות דֶּגֶם: יוֹצֵא דֹּפֶן עָלָה 2.
נְתוּנִים:
  - אבזר: i7, אָדוֹם, אצטרובל וְ־ no
  - אבזר: i0, כָּחוֹל, כַּדּוּר וְ־ no
  - אבזר: i4, אָדוֹם, קובייה וְ־ no
  - אבזר: i1, ירוק מקנאה, אצטרובל וְ־ no
  - אבזר: i6, אָדוֹם, קובייה וְ־ no
  - אבזר: i5, ירוק מקנאה, אצטרובל וְ־ no
  - אבזר: i3, אָדוֹם, אצטרובל וְ־ כֵּן
  - אבזר: i2, אָדוֹם, אצטרובל וְ־ כֵּן
מָשַׁל:
  - מָשַׁל אַחַת תּוֹרָה predict כֵּן כִּי אחד אבזר אםם כַּמָּה שֶׁל its conditions matches אֲשֶׁר אבזר
  - מָשַׁל אחד יוֹצֵא דֹּפֶן הוא אחד אבזר שֶׁל מִי תַּחֲזִית differs מ־ its תָּגִית
  - מָשַׁל total עָלָה הוא תּוֹרָה גֹּדֶל פלוס יוֹצֵא דֹּפֶן עָלָה times מִסְפָּר שֶׁל exceptions
  - מָשַׁל בחר ה־ תּוֹרָה שֶׁל least total עָלָה
איזה mdl אופטימלי תּוֹרָה?