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不確実性と上手く付き合う意思決定の手法
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Takashi Nishibayashi
April 04, 2019
Technology
18
15k
不確実性と上手く付き合う意思決定の手法
予測モデルの不確実性を減らすActive Learning,
モデルの不確実性を予測結果に反映するThompson Sampling,
オンライン最適化など
Takashi Nishibayashi
April 04, 2019
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Transcript
༧ଌͷෆ࣮֬ੑͱ্ख͖͘߹͏ ҙࢥܾఆͷख๏ ެ։൛ 5BLBTIJ/JTIJCBZBTIJ 3FQSP5FDI
͓લͩΕΑ Name: Takashi Nishibayashi twitter.com/@hagino3000 Job: Software Engineer VOYAGE GROUPͰωοτࠂ৴αʔϏε࡞ͬͯ
·͢ɻओʹ৴ϩδοΫ͔Βσʔλੳج൫·Ͱɻ ࠷ۙͷڵຯΦϯϥΠϯҙࢥܾఆͱϝΧχζϜσβ Πϯɻ
࠷ۙͷ׆ಈ ਓೳֶձࢽ Vol. 32 No. 4 (2017/07) ͷʮࠂͱ AI ಛूʯʹʮΞυωοτϫʔΫʹ͓͚Δࠂ৴ܭ
ըͷ࠷దԽʯ͕ܝࡌ͞Ε·ͨ͠ɻ ΦϥΠϦʔ͔ΒʮࣄͰ͡ΊΔػցֶशʯ͕ग़· ͨ͠ɻ @chezou, @tokorotenͱڞஶ ࢴ൛ɾిࢠॻ੶྆ํ͋Γ·͢
ࠓͷ w ༧ଌγεςϜͱҙࢥܾఆ w Ϗδωεʹ͓͚Δ࠷దԽ w ϥϕϧແ͠σʔλͷ୳ࠪ w ༧ଌϞσϧͷෆ͔֬͞Λߦಈʹө͢Δ w
ΦϯϥΠϯ࠷దԽ ػցֶशͰಘͨ༧ଌΛͲͷΑ͏ʹͯ͠͏͔ɺ༧ଌͷ࣍ͷҙࢥܾ ఆͷϑΣʔζʹ͠·͢ɻ࣮ࡍͷΞϓϦέʔγϣϯհͭͭ͠ ΛਐΊ·͢ɻ
༧ଌγεςϜͱҙࢥܾఆ
༧ଌͱҙࢥܾఆͷྫ ༧ଌλεΫ ҙࢥܾఆ ԿͷͨΊʹ धཁ༧ଌ ੜ࢈ܭը ҆શࡏݿ֬อɾࡏݿίετݮ ނোՕॴͷ༧ଌ ϝϯςφϯεܭը ϝϯςφϯεඅ༻ݮ
Ձͷ༧ଌ ചΓങ͍ͷܾఆ औҾ͕ੜΉརӹͷ࠷େԽ ࠂޮՌͷਪఆ ࠂΛද͖͔ࣔ͢Ͳ͏͔ ༧ࢉͰͷࠂޮՌ࠷େԽ Ͱ͖ΕࣗಈͰܾΊ͍ͨɺͰͲ͏͢Ε Ή͠ΖΞϓϦέʔγϣϯΤϯδχΞͷࣄࣗಈԽ͕ϝΠϯ
ཧ࠷దԽ ͋Δ੍ͷݩͰతؔΛ࠷େ ࠷খ Խ͢ΔύϥϝʔλΛٻΊΔ ෆ࣮֬ੑͷແ͍ͱ
*1"ಠཱߦ๏ਓใॲཧਪਐػߏɿࢠɾׂ߹ɾղྫɾ࠾ߨධʢɺฏʣ IUUQTXXXKJUFDJQBHPKQ@IBOOJ@TVLJSVNPOEBJ@LBJUPV@IIUNMBLJ ͋ΔͰදʹࣔ͢Λ͍ͯ͠Δɻ࣮ݱՄೳͳ࠷େརӹԿԁ͔ɻ͜͜Ͱɺ ֤ͷ݄ؒधཁྔʹ্ݶ͕͋Γɺ·ͨɺఔʹ͑Δͷ݄࣌ؒؒ࣌ ؒ·ͰͰɺෳछྨͷΛಉ࣌ʹฒߦͯ͢͠Δ͜ͱͰ͖ͳ͍ͷͱ͢Δɻ جຊใॲཧٕज़ऀࢼݧ)ळقΑΓ 9 : ; ݸͨΓͷརӹ
ԁ ݸ͋ͨΓͷॴ༻࣌ؒ ݄ؒधཁ࠷্ݶ ྫੜ࢈ܭը ֬ఆͨ͠
ެ։൛ࢿྉʹ͖ͭิ ҎԼͷ௨Γܭըͱͯ͠ఆࣜԽͯ͠ղ͚ Yݸ Zݸ [ݸΛ࡞Εརӹ͕࠷େʹͳΔͷ͕Θ͔Δɻ࣮Ͱखܭࢉ͠ͳ͍
༧ଌΛར༻ͨ͠࠷దԽ 9 : ; ݸͨΓͷརӹ ԁ ʙ ݸ͋ͨΓͷॴ༻࣌ؒ
ʙ ݄ؒधཁ࠷্ݶ ࣮ࡍʹ࡞ͬͨΓചͬͯΈΔ·ͰΘ͔Βͳ͍෦ ༧ଌΛར༻͍ͯ͠Δ࣌ͰɺԿΒ͔ͷෆ࣮֬ੑΛแ͍ͯ͠Δ ͦΕͳΓʹ༧ଌͰ͖Δ෦ ͜Μͳঢ়ଶ͔Βελʔτ͢ΔʹͲ͏ͨ͠Β͍͍͔
ࠓհ͢Δओͳํࡦ wҎԼͷ܁Γฦ͠ ༧ଌ ҙࢥܾఆɾߦಈ ݁Ռͷ؍ଌ ༧ଌثͷߋ৽
༨ஊ࠷దͱԿ͔ w ඇࣗ໌Ͱ͋Δࣄ͕ଟ͍ͱײ͡Δ w ࠗ׆ϚονϯάΞϓϦ w Ϛονϯά͕͗͢Δͱࢢ͕ബ͘ͳΔδϨϯϚ w ೖΕՁ֨ w
ʮೖΕՁ֨Λ্͍͛ͨʯʮརӹ૬Ͱ ʯ w ೖΕʹϚʔδϯ Λͤͯച͍ͬͯͨˠೖΕ্͕͕Δͱૈར૿ w ͚ϧʔϧΛม͑Δॴ͔Βͬͨ w ۀͦͷͷΛม͑ΒΕΔ༨͕ͲΕ͚ͩ͋Δ͔
'MJOUࢢͷਫಓަࣄۀ
5IF4FBSDIGPS-FBE1JQFT JO'MJOU .JDIJHBO<> w Ԗڅਫ -FBE1JQFT ͷަΛ͢ΔͨΊʹػցֶश༧ଌϞσϧΛར༻ͨ͠ࣄྫ w ,%%ʹ࠾͞Εͨจʹख๏͕ࡌ͍ͬͯΔ w
എܠ w ԖڅਫԖ༹͕ग़͠ͳ͍Α͏ʹද໘͕ίʔςΟϯά͞Ε͍ͯΔ w 'MJOUࢢʹ͓͍ͯਫݯΛม͑ͨ࣌ʹਫ࣭͕มΘͬͯίʔςΟϯά͕ണ͛ͨ w ਫಓਫͷԖͷ༹ग़ʹΑΔ݈߁ඃ͕ൃੜ w ߦͷهෆਖ਼֬
5IF4FBSDIGPS-FBE1JQFT JO'MJOU .JDIJHBO ଓ͖ w w ͲͷՈʹԖڅਫ͕ΘΕ͍ͯͯɺͦΕͲ͜ʹ͋Δͷ͔ w ݶΒΕͨ༧ࢉΛͲͷΑ͏ʹͯ͠ԖڅਫͷަʹׂΓͯΕ͍͍ͷ͔
w ঢ়گɾ੍ w ਫಓΛ۷Γىͯ֬͠ೝ͢Δίετ͕ߴ͍ ϥϕϧ͚ίετ w ܇࿅σʔλݶΒΕ͓ͯΓɺภ͍ͬͯΔ
'MJOUMFBEQJQFSFQMBDFNFOUQSPHSBNUPTXJUDIIBOETJONMJWFDPN IUUQTXXXNMJWFDPNOFXTqJOUqJOU@MFBE@QJQF@SFQMBDFNFOU@QSIUNM
"CFSOFUIZ +BDPC FUBM"DUJWF3FNFEJBUJPO5IF4FBSDIGPS-FBE1JQFTJO'MJOU .JDIJHBO1SPDFFEJOHTPGUIFUI "$.4*(,%%*OUFSOBUJPOBM$POGFSFODFPO,OPXMFEHF%JTDPWFSZ%BUB.JOJOH"$. ༧ଌ݁ՌΛݩʹௐࠪϙΠϯτΛܾΊΔϧʔϧ ༧ଌ݁ՌΛݩʹύΠϓަϙΠϯτΛܾΊΔϧʔϧ ༧ଌϞσϧ
5IF4FBSDIGPS-FBE1JQFT JO'MJOU .JDIJHBO ଓ͖ w ௐࠪϙΠϯτܾఆϧʔϧ w ใΛऔಘͯ͠༧ଌੑೳΛ্͛Δͷ͕త w ೳಈֶश
"DUJWF-FBSOJOH w ύΠϓަϙΠϯτܾఆϧʔϧ w ޡ۷ίετΛ࠷খԽ͍ͨ͠ w ࠷֬ͷߴ͍ϙΠϯτΛબͿɺᩦཉ๏ (SFFEZ"MHPSJUIN
ೳಈֶश "DUJWF-FBSOJOH w എܠ w ڭࢣ͋Γֶश܇࿅σʔλ͕ଟ͍ఔਫ਼্͕͕Δ w ͨͩ͠ϥϕϧ͚ Ξϊςʔγϣϯ ʹίετ͕͔͔Δ
w Ξϓϩʔν w ༧ଌثͷਫ਼্ʹد༩͢ΔσʔλΛબͿ w ํࡦͷྫ࠷ෆ͔֬ͳσʔλΛબ͢Δ w 'MJOUͰ*NQPSUBODF8FJHIUFE"DUJWF-FBOJOHΛ࠾༻
ᩦཉ๏ (SFFEZ"MHPSJUIN w ࢼߦຖʹͦͷ࣌Ͱ࠷ظใु͕େ͖ͳߦಈΛऔΔํࡦ w FHμΠΫετϥ๏ w ۙࣅղ͕ಘΒΕΔ w ʹΑͬͯϫʔετέʔεͷۙࣅʹཧอূ͕͋Δ
w FHφοϓαοΫ w େମ্ख͍࣮͕͘͘͠༰қͳͷͰΑ͘ΘΕΔ
͞ΒͳΔࠔ w ࢪࡦͷධՁύΠϓަ݅͋ͨΓͷίετݮྔ w ˠ w .ͷઅ w
Ռग़ͨͷͷࢢຽ͕ൃ w ਓؒͷ໋Λٹ͏ͣͩͬͨ"*͕࣏ͱແʹΑͬͯແࢹ͞Εͯ͠·ͬͨ IUUQTOPUFNVEBUBTDJFODFOOEFCEEBGF w ΞϧΰϦζϜΛݟΕΘ͔Δ௨Γɺेͳ༧ࢉ͕͋ΕશॅΛ۷Γฦ͠ ͯݕࠪ͢ΔࣄʹͳΔɻௐࠪ͢Δॱ൪͕ૣ͍͔͍͔ͷҧ͍ɻ w ࠷దͱҰମԿͳͷ͔
༧ଌϞσϧͷෆ͔֬͞Λ өͨ͠ߦಈ
ྦྷੵใुΛ࠷େԽ͍ͨ͠ ࢼߦճ ͋ͨΓճ Q ㅟ εϩοτϚγϯ" εϩοτϚγϯ#
֬QͰͨΓ͕ग़ΔϕϧψʔΠࢼߦΛߟ͑Δɺ͜ͷޙͲ͏͖͔͢ ෳ͋ΔબࢶͦΕͧΕ͔Β֬త JJE ʹใु͕ಘΒΕΔઃఆͰγʔέϯγϟϧʹ ߦಈΛܾΊͯྦྷੵใु࠷େԽΛࢦ͢Λʮ֬తόϯσΟοτʯɺ͜ͷ࣌ ͷબࢶΛʮΞʔϜʯͱݺͿɻ
QͷࣄޙΛݟΔ ύϥϝʔλQͷ #FUB ޭճ ࣦഊճ #͕"ΑΓྑ͍ͱஅ͢Δʹ·ͩϦεΫ͕͋Δ
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ઢܗϞσϧͷ߹ ύϥϝʔλͷਪఆͦΕͧΕҟͳΔޡࠩΛ࣋ͭ සओٛͰ࠷ਪఆྔwΛݻఆͨ͠ύϥϝʔλͱͯ͠͏͕
Results: Ordinary least squares ================================================================== Model: OLS Adj. R-squared: 0.946
Dependent Variable: y AIC: 3196.9303 Date: 2019-04-04 00:32 BIC: 3230.7426 No. Observations: 506 Log-Likelihood: -1590.5 Df Model: 8 F-statistic: 1110. Df Residuals: 498 Prob (F-statistic): 8.68e-312 R-squared: 0.947 Scale: 31.960 -------------------------------------------------------------------- Coef. Std.Err. t P>|t| [0.025 0.975] -------------------------------------------------------------------- CRIM -0.1858 0.0380 -4.8884 0.0000 -0.2605 -0.1111 ZN 0.0833 0.0146 5.7100 0.0000 0.0546 0.1119 CHAS 3.8725 1.0130 3.8227 0.0001 1.8821 5.8629 NOX -18.5928 3.0070 -6.1833 0.0000 -24.5007 -12.6849 RM 6.8287 0.2539 26.8931 0.0000 6.3298 7.3276 DIS -1.3713 0.1736 -7.8985 0.0000 -1.7124 -1.0302 RAD 0.2022 0.0711 2.8420 0.0047 0.0624 0.3420 TAX -0.0180 0.0038 -4.7172 0.0000 -0.0255 -0.0105 ------------------------------------------------------------------ ྫ#PTUPOෆಈ࢈Ձ֨σʔλͷઢܗճؼ #PTUPOIPVTFQSJDFTEBUBTFUΛલॲཧແ͠Ͱ0-4ͨ݁͠Ռ
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