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人工知能と機械学習 / AI and ML
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Yukino Baba
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September 26, 2019
Education
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人工知能と機械学習 / AI and ML
Yukino Baba
PRO
September 26, 2019
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Transcript
ஜେֶใՊֶྨഅઇ೫ CBCB!DTUTVLVCBBDKQ ஜେֶڞ௨Պʢใʣ ʮσʔλαΠΤϯεʯ ਓೳͱػցֶश
/39 ຊߨٛͰֶͿ͜ͱ w ਓೳ͕Ͳ͏͍͏໘Ͱ׆༻͞Ε͍ͯΔͷ͔ w ͍·ͷਓೳʹԿ͕Ͱ͖Δͷ͔ w ػցֶशͰͲͷΑ͏ʹσʔλ͔ΒͷֶशΛߦ͏ͷ͔ ਓೳʹԿ͕Ͱ͖Δͷ͔ɾػցֶशͲ͏͍͏͘͠Έ͔ 2
ਓೳ
/39 ਓೳͱ w ਓೳ ʮతͳػցɺಛʹɺతͳίϯϐϡʔλϓϩάϥϜΛ࡞ΔՊֶͱٕज़ʯ w ैདྷͷίϯϐϡʔλϓϩάϥϜతͰͳ͔ͬͨ w ਓ͕ؒೳΛͬͯͰ͖Δͷͱಉ͜͡ͱΛ ίϯϐϡʔλϓϩάϥϜͰͰ͖ΔΑ͏ʹ͍ͨ͠
ਓೳʮతͳίϯϐϡʔλϓϩάϥϜʯ 4 IUUQTXXXBJHBLLBJPSKQXIBUTBJ"*GBRIUNM (*)
/39 ਓೳͱ w తͰͳ͍ίϯϐϡʔλϓϩάϥϜ ༩͑ΒΕͨϧʔϧͲ͓Γͷಈ࡞͔͠Ͱ͖ͳ͍ ίϯϐϡʔλʹѻ͑ΔܗࣜͰਓ͕ؒϧʔϧΛ࡞Δඞཁ͕͋Δ ෳࡶͳͰͯ͢ͷঢ়گΛཏͨ͠ϧʔϧΛ࡞Δ͜ͱࠔ w తͳίϯϐϡʔλϓϩάϥϜʢਓೳʣ ঢ়گʹԠͯ͡ͲͷΑ͏ʹಈ࡞͢Εྑ͍͔ΛྟػԠมʹஅ͢Δ
ܦݧڭࡐ͔ΒϧʔϧΛֶश͢Δ গͷϧʔϧ͔Βਪͯ͠அ͢Δ ਓೳঢ়گʹԠͯ͡ྟػԠมʹಈ࡞͢Δ 5
/39 w తͰͳ͍ίϯϐϡʔλϓϩάϥϜϧʔϧʹैͬͯೝࣝ͢Δ w ༷ʑͳචͷखॻ͖จࣈΛཏͨ͠ϧʔϧΛ࡞Δͷࠔ ਓೳͱ ྫɿखॻ͖ࣈจࣈΛೝࣝ͢ΔίϯϐϡʔλϓϩάϥϜ 6 खॻ͖ࣈจࣈͷग़యɿIUUQZBOOMFDVODPNFYECNOJTU
1 1 ʮࠨӈࡾͷҰͷྖҬ͕ന৭ͳΒʯ
/39 ਓೳڭࡐʢը૾ͱࣈͷϖΞʣΛͬͯೝࣝϧʔϧΛֶश͢Δ ਓೳͱ ྫɿखॻ͖ࣈจࣈΛೝࣝ͢ΔίϯϐϡʔλϓϩάϥϜ 7 1 1 9 9 7
7 ϧʔϧΛֶश 9 ϧʔϧΛར༻ ڭࡐ ʜ खॻ͖ࣈจࣈͷग़యɿIUUQZBOOMFDVODPNFYECNOJTU
/39 ৗͷதͷਓೳ w εϚʔτεϐʔΧʔɿ ਓؒͷԻࢦࣔʹै͍༷ʑͳಈ࡞Λߦ͏ w ϩϘοτআػɿ োΛආ͚ͳ͕Β෦શମΛআ͢Δ w إೝূɿ
Χϝϥલͷਓ͕ొ͞ΕͨਓͱಉҰਓ͔Ͳ͏͔Λఆ w ίϯςϯπɾਪનɿ Ӿཡཤྺߪങཤྺʹͱ͖ͮϢʔβ͕ΉΞΠςϜΛఏࣔ զʑͷৗͷ͞·͟·ͳ໘Ͱਓೳ͕׆༻͞Ε͍ͯΔ 8
/39 ਓؒΛ͑Δਓೳ w *#.8BUTPOɿ ΞϝϦΧͷΫΠζ൪ʮ+FPQBSEZʯʹઓɺৗ࿈ग़ԋऀΛഁΓ༏উ w ౦ϩϘ͘Μɿ େֶೖࢼࢼͷֶɾੈք࢙Ͱภࠩ͑Λୡ w %FFQ#MVFɿ
νΣεͷੈքνϟϯϐΦϯʹউར w "MQIB(Pɿ ғޟͷੈքτοϓع࢜ʹউར ΫΠζήʔϜͰਓؒΛ͑Δਓೳ͕ొ 9
/39 ݱࡏͷਓೳʹͰ͖Δ͜ͱɿಛԽܕWT൚༻ܕ w ਓೳʮಛԽܕʯͱʮ൚༻ܕʯͷೋछྨʹେผ͞ΕΔ w ಛԽܕਓೳɿ ͋Δಛఆͷঢ়گʹ͓͍ͯతʹ;Δ·͏ਓೳ w ൚༻ܕਓೳɿ
ਓؒͱಉ༷ʹ༷ʑͳঢ়گɾʹ͓͍ͯతͳ;Δ·͍͕Ͱ͖Δਓೳ w ݱࡏͷਓೳಛԽܕਓೳͰ͋Γ ൚༻ܕਓೳະ࣮ͩݱ͞Ε͍ͯͳ͍ ݱࡏͷਓೳಛఆͷ͜ͱ͚ͩͰ͖ΔಛԽܕਓೳ 10
/39 ݱࡏͷਓೳʹͰ͖Δ͜ͱɿը૾ॲཧ w ը૾ೝࣝɿ ࣸਅʹ͍ࣸͬͯΔͷΛೝࣝ͢Δ w ମݕग़ɿ ࣸਅͷͲ͜ʹԿ͕͍ࣸͬͯΔͷ͔Λೝࣝ͢Δ w ը૾ੜɿ
ຊͷࣸਅͷΑ͏ͳը૾Λਓతʹ࡞Γग़͢ ಛԽܕਓೳͰ༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ 11 ग़యɿIUUQDTOTUBOGPSEFEVTMJEFTDTO@@MFDUVSFQEG ग़యɿIUUQTBSYJWPSHBCT ຊͷࣸਅ ਓతͳࣸਅ (*) (*)
/39 ݱࡏͷਓೳʹͰ͖Δ͜ͱɿݴޠॲཧ w จॻྨɿ จॻΛಛఆͷΧςΰϦʹྨ͢Δ w ػց༁ɿ ͋ΔݴޠͰॻ͔ΕͨจষΛผͷݴޠʹ༁͢Δ w ใݕࡧɿ
ಛఆͷใ͕ܝࡌ͞Ε͍ͯΔΣϒϖʔδΛݟ͚ͭΔ w ࣭Ԡɿ จষͰ༩͑ΒΕ࣭ͨʹର͢Δ͑Λฦ͢ ಛԽܕਓೳͰ༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ 12
/39 ݱࡏͷਓೳʹͰ͖Δ͜ͱɿԻॲཧ w Իೝࣝɿ ͞Ε͍ͯΔ༰ΛจষͰॻ͖ى͜͢ w ऀಉఆɿ ొ͞Εͨਓͱಉ͡ਓ͕͍ͯ͠Δ͔ఆ͢Δ w Ի߹ɿ
ਓ͕͍ؒͯ͠ΔΑ͏ͳΛਓతʹ࡞Γग़͢ ಛԽܕਓೳͰ༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ 13
/39 ݱࡏͷਓೳʹͰ͖Δ͜ͱɿϩϘοτ w ڥೝࣝɿ ηϯαใ͔ΒपғʹԿ͕͋Δ͔Λೝࣝ͢Δ w ܦ࿏ɾߦಈܭըɿ Ҡಈɾಈ࡞ࢦࣔʹै͏ͨΊʹͲͷΑ͏ͳܦ࿏Λ௨Δ͔ɺ Ͳͷ෦ΛͲͷΑ͏ʹಈ͔͔͢Λܭը͢Δ ಛԽܕਓೳͰ༷ʑͳ͜ͱ͕࣮ݱͰ͖Δ
14
/39 ਓೳͷ׆༻ྫ ྫᶃεϚʔτεϐʔΧʔ ಛԽܕਓೳͷ߹ͤͰ͞Βʹߴͳ͜ͱΛ࣮ݱͰ͖Δ 15 4UFQىಈΩʔϫʔυͷೝࣝ 4UFQԻʹΑΔࢦࣔΛจষͱͯ͠ೝࣝ 4UFQࢦࣔจ͔ΒతΛೝࣝ 4UFQࢦࣔΛ࣮ߦ
/39 ਓೳͷ׆༻ྫ ྫᶄࣗಈӡస ಛԽܕਓೳͷ߹ͤͰ͞Βʹߴͳ͜ͱΛ࣮ݱͰ͖Δ 16 ը૾ͷग़యɿIUUQTTUPSBHFHPPHMFBQJTDPNTEDQSPEWTBGFUZSFQPSUXBZNPTBGFUZSFQPSUQEG 4UFQଞͷं྆าߦऀͷݕ 4UFQଞͷं྆าߦऀͷߦಈ༧ଌ 4UFQత·Ͱͷܦ࿏Λܭը 4UFQܦ࿏ʹै͏ͨΊͷಈ࡞Λܭը
/39 ਓೳͷ·ͱΊ w ਓೳతͳίϯϐϡʔλϓϩάϥϜͰ ϧʔϧΛֶशɾਪͯ͠ྟػԠมʹಈ࡞Ͱ͖Δ w ͍·ͷਓೳಛԽܕͰ͋Δಛఆͷ͔͠ղ͚ͳ͍ w ༷ʑͳͰಛԽܕਓೳͷݚڀ͕ਐΊΒΕ ͦΕΒͷ߹ͤͰΑΓߴͳਓೳ͕࣮ݱ͞Εͭͭ͋Δ
͍·ͷਓೳಛԽܕ͕༷ͩʑͳ͜ͱ͕Ͱ͖Δ 17
ػցֶश
/39 ػցֶशͱ w ػցֶश ίϯϐϡʔλϓϩάϥϜʹσʔλ͔ΒϧʔϧΛֶशͤ͞Δٕज़ w େྔͷೖग़ྗྫͷσʔλΛڭࡐͱͯ͠༩͑ ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶशͤ͞Δ ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश͢Δ
19 ຊߨٛͰऔΓ্͛Δػցֶशɺݫີʹʮڭࢣ͖ػցֶशʯͱݺΕΔ 7 ೖྗ ग़ྗ ը૾ ࣈ
/39 ػցֶशͱ ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश͢Δ 20 The quick brown fox jumps over
the lazy dog ૉૣ͍৭ͷޅ ͷΖ·ͳݘΛඈͼ ӽ͑Δ “Hello, world!” ೖྗ ग़ྗ ݈߁ϦεΫɿߴ ग़యɿIUUQDTOTUBOGPSEFEVTMJEFTDTO@@MFDUVSFQEG ग़యɿIUUQTNBHFOUBUFOTPSqPXPSHOTZOUIGBTUHFO จষ จষ ը૾ ମͱҐஔ Ի จষ ױऀใ ݈߁ϦεΫ (*) (**)
/39 ػցֶशͱ w εϚʔτϑΥϯɾηϯαɾΠϯλʔωοτɾΣϒαʔϏεͷීٴͰ σʔλ͕૿େͨ͠ͷ͕ػցֶशͷൃలͷ͖͔͚ͬ w ιʔγϟϧϝσΟΞͷߘ ΣϒαʔϏε্Ͱͷਓʑͷߦಈϩάػցֶशͷڭࡐͱͯ͠༻͍ΒΕΔ w ࠂ৴ͳͲͰΣϒαʔϏεӡӦاۀརӹΛ্͛ɺ
ແྉͰΣϒαʔϏεΛఏڙ͠ͳ͕Βڭࡐ༻ͷσʔλΛऩू͍ͯ͠Δ ͋ΒΏΔͷ͕σʔλԽ͞ΕػցֶशͰΘΕΔ 21
/39 ػցֶशͷ͘͠Έɿ܇࿅σʔλ w ྫɿʮजᙾ͕ྑੑ͔ѱੑ͔ʯఆ͢ΔͨΊͷϧʔϧΛֶशͤ͞Δ w ڭࡐͱͯ͠༻͍ΔσʔλΛ܇࿅σʔλͱݺͿ w ֶशͨ͠ϧʔϧΛ༻͍ͯ ܇࿅σʔλʹͳ͍जᙾ͕ྑੑ͔ѱੑ͔Λਖ਼͘͠ఆͰ͖ΔΑ͏ʹ͍ͨ͠ ೖग़ྗྫΛ܇࿅σʔλͱͯ͠༻͍ͯϧʔϧΛֶश͢Δ
22 जᙾը૾ͷग़యɿW. N. Street et al. , Nuclear feature extraction for breast tumor diagnosis. Proc. of the International Symposium on Electronic Imaging, 1993. ྑੑ ѱੑ ྑੑ ѱੑ ܇࿅σʔλ ೖྗʢजᙾʣΛ ग़ྗʢྑੑPSѱੑʣʹ ରԠ͚ͮΔ ϧʔϧ ֶश
/39 ػցֶशͷ͘͠Έɿ܇࿅σʔλ w ίϯϐϡʔλͰܭࢉͰ͖ΔΑ͏ʹ܇࿅σʔλͰදݱ͞Ε͍ͯΔ w ྫɿʮେ͖͞ʯͱʮͰ͜΅͜ʯͰजᙾΛදݱ͢Δ ʢԿΒ͔ͷखஈͰԽ͞Ε͍ͯΔͱ͢Δʣ ܇࿅σʔλͰදݱ͞Εͳ͍ͱ͍͚ͳ͍ 23 ܇࿅σʔλͷྫ
ೖྗ ग़ྗ େ͖͞ Ͱ͜΅͜ जᙾ 0.252 0.014 ྑੑ जᙾ 0.327 0.340 ྑੑ जᙾ 1.000 0.371 ѱੑ जᙾ 0.223 0.369 ѱੑ ʜ ʜ ʜ ʜ
/39 ػցֶशͷ͘͠Έɿ܇࿅σʔλ ܇࿅σʔλͰදݱ͞Εͳ͍ͱ͍͚ͳ͍ 24 211 220 223 … 213 217
217 220 … 210 214 217 216 … 205 … … … … … 216 217 214 … 181 The quick brown fox jumps over the lazy dog aardvark … brown … dog … zzzat 0 … 1 … 1 … 0
/39 ػցֶशͷ͘͠Έɿ܇࿅σʔλ ܇࿅σʔλͰදݱ͞Εͳ͍ͱ͍͚ͳ͍ 25 ܇࿅σʔλͷྫ ೖྗ ग़ྗ େ͖͞ Ͱ͜΅͜ जᙾ
0.252 0.014 ྑੑ जᙾ 0.327 0.340 ྑੑ जᙾ 1.000 0.371 ѱੑ जᙾ 0.223 0.369 ѱੑ ʜ ʜ ʜ ʜ ਤࣔ େ͖͞ Ͱ͜΅͜ ྑੑͷजᙾ ѱੑͷजᙾ जᙾ जᙾ
/39 ػցֶशͷ͘͠Έɿϧʔϧ w ίϯϐϡʔλ͕ܭࢉͰ͖ΔΑ͏ʹϧʔϧࣜͰදݱ͞ΕΔ w ୯७ͳϧʔϧͷܗࣜɿ ϧʔϧࣜͰදݱ͞Εͳ͍ͱ͍͚ͳ͍ 26 େ͖͞
Ͱ͜΅͜ ͳΒྑੑɺͦΕҎ֎ͳΒѱੑ z = w1 × + w2 × + b; z < 0
/39 ػցֶशͷ͘͠Έɿϧʔϧ ϧʔϧࣜͰදݱ͞Εͳ͍ͱ͍͚ͳ͍ 27 େ͖͞ Ͱ͜΅͜ ྑੑͷजᙾ ѱੑͷजᙾ [ʾͷྖҬ [ͷྖҬ
ͷ߹ɿ େ͖͞ Ͱ͜΅͜ w1 = 1, w2 = − 1, b = 0 z = − େ͖͞ Ͱ͜΅͜ z = w1 × + w2 × + b;
/39 ػցֶशͷ͘͠Έɿϧʔϧ X X Cͷ͕มΘΔͱϧʔϧมΘΔ 28 େ͖͞ Ͱ͜΅͜ ྑੑͷजᙾ ѱੑͷजᙾ
[ʾͷྖҬ [ͷྖҬ ͷ߹ɿ େ͖͞ Ͱ͜΅͜ w1 = 1, w2 = 1, b = − 1 z = + −1 େ͖͞ Ͱ͜΅͜ z = w1 × + w2 × + b;
/39 ػցֶशͷ͘͠Έɿϧʔϧ ܇࿅σʔλʹͯ·Δྑ͍ϧʔϧΛֶश͍ͨ͠ 29 େ͖͞ Ͱ͜΅͜ େ͖͞ Ͱ͜΅͜ w1
= 1, w2 = − 1, b = 0 w1 = 1, w2 = 1, b = − 1 ϧʔϧ͕ͯ·Βͳ͍जᙾ ӈͷϧʔϧͷํ͕ɺ܇࿅σʔλʹͯ·Δ
/39 ػցֶशͷ͘͠Έɿଛࣦ ϧʔϧͷྑ͠ѱ͠ΛͰද͢ 30 ଛࣦɿେ ଛࣦɿখ େ͖͞ Ͱ͜΅͜ େ͖͞ Ͱ͜΅͜
w ଛࣦɿϧʔϧͷѱ͞ΛͰදͨ͠ͷ w ܇࿅σʔλʹͯ·Βͳ͍ϧʔϧଛࣦ͕େ͖͘ͳΔ
/39 ػցֶशͷ͘͠Έɿଛࣦ w ʮޡఆͷ݅ʯΛଛࣦͱͯ͠͏ͱϧʔϧͷࡉ͔͍ҧ͍͕Θ͔Βͳ͍ w ϧʔϧʹʮѱੑͷ֬ʯΛग़ྗͤ͞ɺͦΕʹͱ͖ͮଛࣦΛࢉग़ ϧʔϧͷྑ͠ѱ͠ΛͰද͢ 31 ૯Λϧʔϧͷଛࣦͱ͢Δ ଛࣦɿ
܇࿅σʔλ [ ѱੑͷ֬ ଛࣦ ೖྗ ग़ྗ େ͖͞ Ͱ͜΅͜ जᙾ 0.252 0.014 ྑੑ -0.542 0.336 0.410 जᙾ 0.327 0.340 ྑੑ -0.172 0.457 0.611 जᙾ 1.000 0.371 ѱੑ 1.208 0.770 0.261 जᙾ 0.223 0.369 ѱੑ -0.348 0.414 0.882 ʜ ʜ ʜ ʜ a = σ(z)
/39 ػցֶशͷ͘͠Έɿޯ๏ w ଛࣦ͕࠷খͷϧʔϧɺ ͭ·Γ܇࿅σʔλʹ࠷ͯ·ΔϧʔϧΛݟ͚ͭΔͷֶ͕श w ޮతʹͦͷΑ͏ͳϧʔϧΛݟ͚ͭΔͨΊʹޯ๏͕༻͍ΒΕΔ w ޯ๏Ͱɺଛࣦ͕࠷খ͘͞ͳΔํʹগ͚ͩ͠ਐΉ ʢX
X CͷΛগ͚ͩ͠มԽͤ͞Δʣ͜ͱΛ܁Γฦ͢ ଛࣦ͕࠷খͱͳΔϧʔϧʢX X CͷʣΛݟ͚ͭΔ 32
/39 ػցֶशͷ͘͠Έɿ൚Խೳྗ w ֶशͷͦͦͷత ʮ܇࿅σʔλʹͳ͍ະͷೖྗʹରͯ͠ਖ਼͍͠ग़ྗΛฦ͢ʯϧʔϧͷֶश w ܇࿅σʔλͷͯ·Γ͚ͩΛߟྀ͢Δͱ ϧʔϧ͕܇࿅σʔλʹͯ·Γ͗ͯ͢ະͷೖྗͰؒҧ͑ΔڪΕ w X
Xͷ͕ۃʹେ͖͍ϧʔϧաద߹͍ͯ͠ΔڪΕ͕͋ΔͷͰ ͦͷΑ͏ͳϧʔϧʹϖφϧςΟΛ༩͑ΔΑ͏ʹଛࣦΛઃܭ͢Δ w ʮϧʔϧ͕ະͷೖྗʹର͠ਖ਼͍͠ग़ྗΛฦ͢ೳྗʯΛ ൚Խೳྗͱ͍͏ ܇࿅σʔλʹͯ·Γ͗͢Δϧʔϧ൚Խೳྗ͕͍ 33
/39 w ઢͰදݱ͞ΕΔϧʔϧೖྗͱग़ྗͷରԠ͚͕ͮෳࡶͳ߹ʹෆे w ਂֶशΛ༻͍ΔͱෳࡶͳϧʔϧΛֶशͰ͖Δ େ͖͞ Ͱ͜΅͜ େ͖͞ Ͱ͜΅͜ ୯७ͳϧʔϧ
ػցֶशͷ͘͠Έɿਂֶश ෳࡶͳϧʔϧͷֶशʹ༻͍ΒΕΔͷ͕ਂֶश 34 େ͖͞ Ͱ͜΅͜ ਂֶशʹΑΔϧʔϧ
/39 w ୯७ͳϧʔϧͷࣜਤɿ ػցֶशͷ͘͠Έɿਂֶश ਂֶशͰෳͷඇઢܗؔΛଟʹॏͶΔ 35 େ͖͞ Ͱ͜΅͜ ྑੑ·ͨѱੑ w1
w2 a=σ(z) େ͖͞ Ͱ͜΅͜ ྑੑ·ͨѱੑ w ਂֶशʹΑΔϧʔϧͷࣜਤɿ
/39 ػցֶशͷ࣮ w ػցֶशͷ࣮ʹɺϓϩάϥϛϯάݴޠ1ZUIPO͕͘ΘΕ͍ͯΔ w ྫ͑TDJLJUMFBSOͱ͍͏1ZUIPOϥΠϒϥϦΛ͏ͱɺ ߦͷίʔυͰػցֶशΛ࣮͢Δ͜ͱ͕Ͱ͖Δ w ͍͔ͭ͘ͷαϯϓϧσʔλTDJLJUMFBSOͰఏڙ͞Ε͍ͯΔ ༷ʑͳϥΠϒϥϦ͕ެ։͞Ε͓ͯΓ؆୯ʹ࣮͕Ͱ͖Δ
36 https://scikit-learn.org/ ϧʔϧͷֶश ϧʔϧͷద༻
/39 ػցֶशͷ࣮ w ਂֶशͷ1ZUIPOϥΠϒϥϦ༷ʑͳͷ͕ఏڙ͞Ε͍ͯΔɿ 5FOTPS'MPX ,FSBT 1Z5PSDI $IBJOFS w (PPHMF$PMBCPSBUPSZΛ͏ͱԾϚγϯΛͬͯ
ਂֶशϥΠϒϥϦΛΣϒϒϥβ͔Β؆୯ʹ͏͜ͱ͕Ͱ͖Δ ༷ʑͳϥΠϒϥϦ͕ެ։͞Ε͓ͯΓ؆୯ʹ࣮͕Ͱ͖Δ 37 https://colab.research.google.com
/39 ػցֶशͷ࣮ w ,BHHMFͳͲͷػցֶशίϯϖςΟγϣϯͰ ࣮ફతͳػցֶशͷ՝ɾσʔλ͕ఏڙ͞Εଞऀͱڝ͏͜ͱ͕Ͱ͖Δ ػցֶशίϯϖςΟγϣϯͰྗࢼ͠Λ͢Δ͜ͱ͕Ͱ͖Δ 38 https://www.kaggle.com/c/titanic/leaderboard
/39 ػցֶशͷ·ͱΊ w େྔͷೖग़ྗྫͷσʔλΛ܇࿅σʔλͱ͍ͯ͠ ೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश w ܇࿅σʔλʹͰ͖Δ͚ͩͯ·ΔΑ͏ͳϧʔϧ ʢଛࣦͷখ͍͞ϧʔϧʣΛޯ๏Λ༻͍ͯݟ͚ͭΔ ܇࿅σʔλΛ༻͍ͯೖྗͱग़ྗΛରԠ͚ͮΔϧʔϧΛֶश 39