Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
[計算機構論] Why do tree-based models still outperfo...
Search
mei28
December 06, 2022
0
54
[計算機構論] Why do tree-based models still outperform deep learning on tabular data?
計算機構論の発表資料
Why do tree-based models still outperform deep learning on tabular data?(NeurIPS2022)
mei28
December 06, 2022
Tweet
Share
More Decks by mei28
See All by mei28
[読み会] “Are You Really Sure?” Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making
mei28
0
83
[JSAI'24] 人間の判断根拠は文脈によって異なるのか?〜信頼されるXAIに向けた人間の判断根拠理解〜
mei28
1
490
[CHI'24] Fair Machine Guidance to Enhance Fair Decision Making in Biased People
mei28
0
55
[DEIM2024] 卓球の得点予測における重要要素の分析
mei28
0
37
[Human-AI Decision Making勉強会] 意思決定 with AIは個人vsグループで変わるの?
mei28
0
200
[読み会] Words are All You Need? Language as an Approximation for Human Similality Judgements
mei28
0
36
[参加報告] AAAI'23
mei28
0
88
[計算機構論] Learning Models of Individual Behavior in Chess
mei28
0
70
チーム開発と機械学習
mei28
0
53
Featured
See All Featured
Intergalactic Javascript Robots from Outer Space
tanoku
270
27k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
7
640
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
30
4.6k
I Don’t Have Time: Getting Over the Fear to Launch Your Podcast
jcasabona
32
2.1k
[RailsConf 2023] Rails as a piece of cake
palkan
53
5.3k
Embracing the Ebb and Flow
colly
84
4.6k
Git: the NoSQL Database
bkeepers
PRO
427
65k
Documentation Writing (for coders)
carmenintech
67
4.6k
Building a Modern Day E-commerce SEO Strategy
aleyda
38
7.1k
Optimising Largest Contentful Paint
csswizardry
34
3.1k
Testing 201, or: Great Expectations
jmmastey
42
7.2k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
12
990
Transcript
ܭࢉػߏ! ༶໌ 8IZEPUSFFCBTFENPEFMTTUJMM PVUQFSGPSNEFFQMFBSOJOHPOUBCVMBSEBUB
ࣗݾհ • ໊લɿ༶໌ • ॴଐɿ૯߹จԽݚڀՊҬՊֶઐ߈അݚڀࣨ% ʢ݄ೖֶͰஜେ͔Βདྷ·ͨ͠ʣ • ݚڀ༰ɿਓؒͱػց͕ڠௐͰ͖ΔΑ͏ͳػցֶश
ɹɹɹɹˠम࢜Ͱɼҙࢥܾఆͷެฏੑ͕ςʔϚ • ͻͱ͜ͱɿྑ͍ͯͩ͘͘͠͞ʂ
จใ • ϑϥϯεͷํʑɼ*OSJB4BDMBZύϦαΫϨʔେֶͱ͔ ͱ࿈ܞ͍ͯ͠Δݚڀػؔ • બΜͩཧ༝ • ,BHHMFͳͲͰʮॳख-JHIU(#.ʯ͕ओྲྀͰ͋Γɼ.-1 ͕ڧ͍ͱݶΒͳ͍ཧ༝ΛΓ͔͔ͨͬͨΒɽ
͜ͷจ͔ΒಘΒΕΔͷ ͳͥςʔϒϧσʔλͰɼਂϞσϧܾఆΛ ྇կͰ͖ͳ͍ͷ͔ ˠ࣮ݧΛ௨ͯ͠ཧ༝Λߟ͍ͯ͘͠ ςʔϒϧσʔλͷͨ͘͞ΜͷϕϯνϚʔΫΛ࡞ • ༻ͨ͠σʔληοτެ։
• ͞ΒʹɼϋΠύϥௐࡁΈͷϞσϧެ։
༻͢Δ༻ޠʹ͍ͭͯ • ܾఆɿ෦ͷΞϧΰϦζϜ͕ܾఆͳͷશͯΛ͢͞ • //Tɿଟύʔηϓτϩϯਂχϡʔϥϧωοτϫʔ ΫͳͲɼܾఆͰͳ͍ΞϧΰϦζϜΛࢦ͢ɽ
എܠɿςʔϒϧσʔλʹܾఆ͕ओྲྀ ը૾ɼࣗવݴޠɼԻͳͲͷͰɼ ਂֶशϞσϧͷੑೳ͕΄ͱΜͲ4P5"Λͱ͍ͬͯΔ ҰํͰɼςʔϒϧσʔλͰ͍·ͩʹܾఆ͕ڧ͍ //Tσʔλʹରͯ͠ɼؼೲόΠΞεΛڧ͘ੜΉ͕͋ Δ
• ؼೲόΠΞεɿϞσϧʹΑΔԿΒ͔ͷ੍ʢFHઢܗʣ
എܠɿදܗࣜʹಛԽͨ͠//Tͳ͍Θ͚Ͱͳ͍ ςʔϒϧσʔλΛରͱͨ͠//Tݚڀ͞Ε͍ͯΔ ఏҊख๏ʹΑͬͯɼैདྷͷܾఆΑΓ ༏ҐͰ͋Δओுଟ͘ଘࡏ • ಛఆͷσʔληοτʢఏҊऀ࡞ͳͲʣʹͷΈ༏Ґ •
ͨ·ͨ·ϋΠύϥ͕Α͔ͬͨͷͰ ධՁํ๏͕ΒΒͳͷͰɼൺֱͰ͖ͳ͔ͬͨ ˠຊจͰɼϕϯνϚʔΫΛ࡞͢Δʂ
എܠɿܾఆ͕ͳͥڧ͍ͷ͔ //T͕ͳͥऑ͍ͷ͔ //T͕ςʔϒϧσʔλʹ͓͍ͯؼೲόΠΞεʹΑͬͯɼ ੑೳ্͕͍ͯ͠ͳ͍ͷܦݧతʹΒΕ͍ͯΔ ͔͠͠ɼͲΜͳཁૉ͕ؼೲόΠΞεΛͨΒ͍ͯ͠Δ͔ ɼಥ͖ࢭΊ͍ͯΔͷͳ͍ɽ ˠຊจʹΑͬͯ͜ͷݪҼʹ͍ͭͯಥ͖ࢭΊΔ
ຊݚڀͷߩݙ ςʔϒϧσʔλΛ༻͍ͨϕϯνϚʔΫΛ࡞ɽ • ϋΠύϥௐͨ͠Ϟσϧར༻ՄೳͳܗͰެ։ //TͱܾఆΛطଘͷσʔληοτΛ༻͍ͯൺֱ ͍ΖΜͳ݅ઃఆɼϋΠύϥௐΛߦ͏ɽ
ςʔϒϧσʔλͰͳܾͥఆ͕//TΑΓ ڧ͍ͷ͔Λಥ͖ࢭΊͨ
ϕϯνϚʔΫσʔληοτΛ࡞Δ • ϞσϧͷൺֱΛߦ͏ͨΊʹɼϕϯνϚʔΫΛ࡞Δɽ • ࠓճ͞·͟·ͳυϝΠϯͷςʔϒϧσʔλΛछྨ࡞ ͢Δɽ •
Ͳͷσʔληοτ0QFO.-Ͱఏڙ͞Ε͍ͯΔɽ • ࣍ͷϖʔδͷ͜ͱʹҙͯ͠બΜͩ
σʔληοτબʢʣ )FUFSPHFOFPVTDPMVNO • ֤ྻੑ࣭ͱͯ͠ҟͳΔը૾ηϯαʔใෆՄ /PUIJHI%JNFOTJPOBM • σʔληοτͷରͯ͠ߴ࣍ݩͰͳ͍࠷ߴͰ࣍ݩ
6OEPDVNFOUFEEBUBTFUT • ใྔҙຯ͕Θ͔Βͳ͍σʔλΛΘͳ͍ ҉߸Խ͞Ε͍ͯΔ
σʔληοτબʢʣ **%EBUB • ࣌ܥྻσʔλͳͲɼ**%Ͱͳ͍σʔλΘͳ͍ 3FBMXPSMEEBUB • γϛϡϨʔγϣϯਓσʔλ༻͍ͳ͍ɽ
/PUUPPTNBMM • ಛྔ͕ҎԼ αϯϓϧ͕ҎԼΘͳ͍
σʔληοτબʢʣ /PUUPPFBTZ • ༧ଌ͕؆୯ͳσʔληοτΘͳ͍ • σϑΥϧτͷϩδεςΟοΫճؼͱ3FT/FUͷείΞ͕ ૬ରͰҎͩͬͨΒ࠾༻͠ͳ͍ɽ /PUEFUFSNJOJTUJD
• ༧ଌ͕σʔλʹରܾͯ͠ఆతʹܾ·Δͷ༻͍ͳ͍ • ϙʔΧʔνΣεͷήʔϜใͳͲ
෭࣍తͳղফ͢Δʢʣ ςʔϒϧσʔλΛֶश͢ΔλεΫͱͯ͠Γ͚ΔͨΊʹ ࣍ͷʹؾΛ͚ͭΔ .FEJVNTJ[FEUSBJOJOHTFU • αϯϓϧʹͳΔΑ͏ʹσʔλΛΔ
/PNJTTJOHEBUB • ܽଛશͯআɽ
෭࣍తͳղফ͢Δʢʣ #BMBODFEDMBTTFT • ྨλεΫʹ͓͍ͯɼϥϕϧൺಉఔʹ -PXDBSEJOBMJUZDBUFHPSJDBMGFBUVSF • ྻͷதʹछྨҎ্ͷΧςΰϦมআ )JHIDBSEJOBMJUZOVNFSJDBMGFBUVSF •
ྻͷதʹछྨҎԼͷมআ • ͔ͭ͠ͳ͍࣌ΧςΰϦมͱͯ͠ѻ͏
ϋΠύϥௐ ϋΠύϥௐɼϞσϧੑೳ্ʹͪΐͬͱߩݙ͢Δ ϋΠύϥௐʹؔͯ͠)ZQFSPQUΛͬͨ ϥϯμϜαʔνΛ͢Δ ֤σʔληοτͰΠςϨʔγϣϯͰύλʔϯߦ͍ɼྑ ͔ͬͨͷΛ࠷ྑϋΠύϥͱ͢Δ ࣌ؒతʹ ίϯϐϡʔλ࣌ؒͷ݁ՌΛެ։ͨ͠
ධՁࢦඪ ྨͰਫ਼ɼճؼͰ3είΞΛධՁࢦඪʹ͢Δ ҟͳΔσʔληοτͰείΞΛൺֱ͢Δͷࠔ • ճؼͩͱɼΒ͖ͭ߹͍͕͜ͱͳΔͨΊ ˠ"WFSBHFEJTUBODFUPUIFNJOJNVNΛ࠾༻͢Δ • ࠷ྑͱ࠷ѱͷείΞΛͱʹਖ਼نԽ͢Δ
σʔλͷલॲཧ ೖྗʹ༻͍ΔࡍͷલॲཧΛҰ؏ͯ͠ߦ͏ (BVTTJBOJ[FEGFBUVSF • มਖ਼نʹج͍ͮͯ࠶ม 5SBOTGPSNFESFHSFTTJPOUBSHFUT • తมΛมֶ͠शɽਪ࣌ٯม͢͠ 0OF)PU&ODPEFS
• ΧςΰϦมϫϯϗοτԽ͢Δ
ର߅͢ΔܾఆͷҰཡ ʢจதʹॻ͔Ε͍ͯͳ͔ͬͨͷͰਪͰ͢ʣ 9(#PPTUɿͨͿΜ(16ʹରԠ͍ͯ͠Δ͔Β 3BOEPN'PSFTUɿݹయత͕ͩڧ͍ (SBEJFOU#PPTUJOH5SFFɿΧςΰϦมѻ͑Δ
ର߅͢Δ//TͷҰཡ .-1ɿ3FEVDF0O1MBUFBVͷεέδϡʔϥΛՃ 3FT/FUɿ.-1 ESPQPVU CBUDIMBZFSOPSNBMJ[F TLJQDPOOFDUJPO '5@5SBOTGPSNFSɿมΛຒΊࠐΊΔUSBOTGPSNFSϞσϧ
ςʔϒϧσʔλͰҰ൪༗ྗ 4"*/5ɿ5SBOTGPSNFS JOUFSTBNQMFTBUUFOUJPOMBZFS ɹɹɹɹPVUQFSGPSN͢Δ͔Β࠾༻
݁ՌɿมͷΈͷͱ͖ • υοτઢσϑΥϧτ • ϋΠύϥௐͯ͠//T4P5"ʹͳΒͳ͍ • ࣮ߦ࣌ؒॻ͍ͯͳ͍͕ɼܾఆͷํ͕ૣ͘ऴΘΔ
݁Ռɿม ΧςΰϦม • طଘݚڀͰදܗࣜͷΧςΰϦมͷѻ͍͕૪Ͱ͋ͬͨ • ΧςΰϦมͷ͍ͤͰ//T͕ऑ͍Θ͚Ͱͳ͍
ͳͥ//T͕ܾఆʹউͯͳ͍͔Λߟ͍͑ͯ͘ ϕϯνϚʔΫ࣮ݧʹΑΓɼ//T͕ܾఆʹউͯͳ͍͜ͱΛ ࠶֬ೝͨ͠ ͔͜͜ΒɼςʔϒϧσʔλͷಛྔΛมԽͤ͞ɼҧ͍ʹ ͍ͭͯ୳ڀ͍ͯ͘͠ ඪɿදܗࣜͷͲ͏͍͏ಛ͕ܾఆʹ༗ޮͰ //TͰ͍ͯ͘͠͠Δཁૉͳͷ͔Λݟ͚ͭΔ
//Tͷग़ྗΒ͔ʹͳΔΑ͏ʹภΔ ֤܇࿅ͷηοτͷग़ྗʹରͯ͠ɼΧʔωϧฏԽΛߦ͏ɽ • Χʔωϧฏ͔Λߦ͏ͱɼ֎Εͷ༧ଌΛܰݮ͢Δ͜ͱ ͕Մೳɽ • ΧʔωϧฏԽۙ͘ͷͷͷ͍ۙΠϝʔδ
//Tͷग़ྗΒ͔ʹͳΔΑ͏ʹภΔ • ฏԽͷ෯Λมߋ͢Δͱɼ ܾఆͷਫ਼͕ஶ͘͠མͪ ͨ • ҰํͰ//T͕ࠩখ͍͞ ˠͱͱ//T͕ࣗΒ͔ ͳग़ྗΛߦ͏ؼೲόΠΞε͕
ଘࡏ
//Tͷग़ྗΒ͔ʹͳΔΑ͏ʹภΔ
//Tෆඞཁͳใʹऑ͍ • ϥϯμϜϑΥϨετʹΑͬͯϥϯΫ͚ͨ͠ಛྔॏཁ ʹԠͯ͡ಛྔΛܽམͨ࣌͠ͷੑೳࠩʹ͍ͭͯݟ͍ͯ͘ • (#%5ͰɼಛྔΛܽམͤͯ͞ɼਫ਼Լ͋· Γى͖ͳ͔ͬͨ • ܽམͤͨ͞ಛྔͷΈͰֶशͯ͠ਫ਼͋·Γ্͕Βͳ
͍ ˠ͖ͪΜͱඞཁͳใͷΈΛֶͬͯश͍ͯ͠Δ
//Tෆඞཁͳใʹऑ͍ • ಛྔͷܽམͱɼඇใͳಛྔΛՃͨ࣌͠ͷൺֱ • //Tඇใಛྔʹରͯ͠ؤ݈Ͱͳ͍
දܗࣜʹճసෆมੑ͕ͳ͘ɼ//Tͷֶशʹ͔ͳ͍ • //TճసෆมੑͷಛΛ࣋ͭɽͭ·ΓҙͷϢχλϦ ߦྻΛೖྗʹ͔͚ͯग़ྗʹӨڹ͕গͳ͍ɽ ճసෆมੑɿೖྗ͕ճసͯ͠ɼຊ࣭มΘΒͳ͍ ✖ ϢχλϦߦྻͷྫ
දܗࣜʹճసෆมੑ͕ͳ͘ɼ//Tͷֶशʹ͔ͳ͍ • σʔληοτΛϥϯμϜʹճసͤ͞ɼਫ਼ͷมԽΛݟΔ
·ͱΊ • ςʔϒϧσʔλʹ͓͍ܾͯఆͱ//Tͷҧ͍ΛΈ͚ͭͨ //Tͷ༧ଌΒ͔ʹͳΔؼೲόΠΞε //Tෆඞཁͳใʹऑ͍ දܗࣜͷճసෆมੑ͕ͳ͍͜ͱ͕//TͱϛεϚον • දܗࣜͷσʔληοτͱϋΠύϥௐࡁΈϞσϧΛެ։ ײɿ
• ৽ख๏ΛఏҊͨ͠Θ͚Ͱͳ͍͕ɼ࣮ݧͱߟ͕ஸೡ • ͓ۚͱ࣌ؒΛͨ͘͞Μඅ͍ͯ͠Δͷ͕͏͔͕͑ͨ
ϒϥβૢ࡞͢ΔͷʹϚε͍ͬͯΒͳ͘ͳ͍ʁʁ $ISPNFͷ֦ுػೳʮ7JNJVNʯΛ͓͢͢Ί͠·͢ʂʂ ϚεͷҠಈڑLN͋ΔΈ͍ͨ<>ɹ • ຖ͜Μͳಈ͔͢ͷ͠ΜͲ͍ • ΩʔϘʔυͱϚεͷԟ෮ແବ͡Όͳ͍ʁ ͣͬͱΩʔϘʔυ্ʹखΛஔ͍ͯ࡞ۀͰ͖ͨΒ
ΜΓͳͷʹͳ͊ <>IUUQTEBJMZQPSUBM[KQLJKJNPVTF@QPJOUFSNPWFNFOU@EJTUBODF
ϒϥβૢ࡞͢ΔͷʹϚε͍ͬͯΒͳ͘ͳ͍ʁʁ ͦΕɼʮWJNJVNʯͰͰ͖·͢ ϒϥδϯάͷϚεૢ࡞Λ ΩʔϘʔυͷΩʔૢ࡞Ͱସ͢Δ͜ͱ͕Մೳʂ • ૢ࡞ײWJNʹ͍ۙͨΊɼWJNNFS͙͢׳ΕΔʂ ݸਓతʹWJNJVNͷΦϜχݕࡧ͕ΊͬͪΌศར IUUQTDISPNFHPPHMFDPNXFCTUPSFEFUBJMWJNJVNECFQHHFPHCBJCIHOIIOEPKQFQJJIDNFC