Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
FukuokaR #7
Search
Hiroki Mizukami
March 25, 2017
Science
350
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
FukuokaR #7
https://www.amazon.co.jp/dp/4774188778
Hiroki Mizukami
March 25, 2017
More Decks by Hiroki Mizukami
See All by Hiroki Mizukami
音楽配信サービスにおける 推薦システムの概要と 数理モデルについて
hiroki_mizukami
0
240
CADEDA #6 AWAにおけるデータ利活用の取り組みと今後の展望について
hiroki_mizukami
4
2.5k
オンライン広告の数理モデルと数学ソフトウェア MSFD#23
hiroki_mizukami
6
4.8k
Other Decks in Science
See All in Science
Where does the rigor go? Research software and the future of trustworthy science.
arfon
0
250
[TMLR 2026, Featured Certification] Double Bounded α-Divergence Optimization for Density Estimation
gkazunii
1
120
Leitner Inauguration Lecture Chalmers University of Technology
xleitix
0
350
明治薬科大学講義_ビッグデータ解析を支えるデータベース技術とクラウドコンピューティング
ktatsuya
1
180
データベース02: データベースの概念
trycycle
PRO
2
1.4k
データベース04: SQL (1/3) 単純質問 & 集約演算
trycycle
PRO
0
1.7k
[Webinaire InnOvin] Coup de Chaud : Comment préserver la santé des animaux
institudelelevage
PRO
0
110
[第67回 CV勉強会@関東] CV × Scientific Figures / kantoCV 67th CVPR 2026
lychee1223
0
210
人生を変えた一冊「独学大全」のはなし / Self-study ENCYCLOPEDIA: The Book Which Change My Life #独学大全 #EM推し本
expajp
0
210
機械学習 - ニューラルネットワーク入門
trycycle
PRO
0
1.3k
データベース05: SQL(2/3) 結合質問
trycycle
PRO
0
1.3k
データベース12: 正規化(2/2) - データ従属性に基づく正規化
trycycle
PRO
0
1.7k
Featured
See All Featured
Rails Girls Zürich Keynote
gr2m
96
14k
コードの90%をAIが書く世界で何が待っているのか / What awaits us in a world where 90% of the code is written by AI
rkaga
63
46k
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
230
23k
Joys of Absence: A Defence of Solitary Play
codingconduct
1
520
A Tale of Four Properties
chriscoyier
163
24k
Dealing with People You Can't Stand - Big Design 2015
cassininazir
367
27k
Design in an AI World
tapps
1
320
Highjacked: Video Game Concept Design
rkendrick25
PRO
1
460
WCS-LA-2024
lcolladotor
0
830
Data-driven link building: lessons from a $708K investment (BrightonSEO talk)
szymonslowik
1
1.3k
Measuring & Analyzing Core Web Vitals
bluesmoon
9
1k
AI Search: Where Are We & What Can We Do About It?
aleyda
0
7.9k
Transcript
Ӣ Ӣ Ӣ Ӣ Ӣ Ӣ Ӣ Ӣ Ӣ Ӣ
Ӣ ֗ ֗ ֗ ֗ ֗ ֗ ֗ ֗ ֗ ֗ ొ ཽ
ཽ ܭ ౷ ొ ཧ ֬ 2
ཧϞσϦϯάͱɺ ౷ܭϞσϦϯάͱɺ ͦΕ͔Βɺࢲɻ 2
ཽ ܭ ౷ ొ ཧ ֬ 3
@ Fukuoka R Mar 25, 2017 य़ Hiroki Mizukami Destroy 3
ཽ ܭ ౷ ొ ཧ ֬ 4
※ݸਓͷݟղɻɻɻ 4
ཽ ܭ ౷ ొ ཧ ֬ 5
5 ࣗݾհͱ͝ΊΜͳ͍͞ ౷ܭϞσϦϯά ༧ଌͱ൚Խ ઢܗճؼϞσϧ ·ͱΊ ཧϞσϦϯά ղऍͱ൚Խ
• Έ͔ͣΈ ͻΖ͖ • LINE_ID: @piroyoung • αΠόʔܥͷAI Labɽ •
αʔόαΠυΤϯδχΞ • σʔλαΠΤϯςΟετ • ౦ژࡏॅʗԬग़ • ֶʗࠂʗWeb • Love έΰύʔΫ • R/Python/Scala/javascript/Spark/ Docker/AWS/Stan/Tableau/AWS/GCP ࣗݾհ ϔϏϝλ
Rݴޠ ʢڱٛʣ Rݴޠʢ͋ʔΔ͛Μ͝ʣΦʔϓϯιʔεɾϑϦʔιϑτΣΞͷ౷ܭղੳ͚ ͷϓϩάϥϛϯάݴޠٴͼͦͷ։ൃ࣮ߦڥͰ͋Δɻ RݴޠχϡʔδʔϥϯυͷΦʔΫϥϯυେֶͷRoss IhakaͱRobert Clifford GentlemanʹΑΓ࡞ΒΕͨɻݱࡏͰR Development Core
TeamʢSݴޠ։ൃऀ Ͱ͋ΔJohn M. Chambersࢀը͍ͯ͠Δ[1]ɻʣʹΑΓϝϯςφϯεͱ֦ு͕ͳ ͞Ε͍ͯΔɻ RݴޠͷιʔείʔυओʹCݴޠɺFORTRANɺͦͯ͠RʹΑͬͯ։ൃ͞Εͨɻ - wikipedia -
Rݴޠ ʢٛʣ σʔλੳΛੜۀͱ͢Δܑ͓͞Μ͓Ͷ͐͞ΜୡͷίϛϡχςΟͷ૯শɾ֓೦ɾε ϥϯάɻདྷΔͷશͯڋ·ͳ͍ελΠϧͰɺ࣮ࡍʹσʔλੳΛ͍ͬͯΔ͔ ͢ΒجຊతʹࣗݾਃࠂɻϢʔϞΞͱϢʔϞΞͱਓฑ͕ΛूΊΔϙΠϯτɽෳ ͷελʔτΞοϓϕϯνϟʔΛੜΈग़͍ͯ͠Δɽ ͱ͋Δ౷ܭʹΑΔͱ࣮ࡍʹRΛ͔ͭͬͯΔͻͱ Α͏͢ΔʹɼࠓRͷίΞͳ͠ͳ͍ͬͯ͜ͱͰ͢͢Έ·ͤΜɽ - mikipedia
-
ཽ ܭ ౷ ొ ཧ ֬ 9
9 ࣗݾհͱ͝ΊΜͳ͍͞ ౷ܭϞσϦϯά ༧ଌͱ൚Խ ઢܗճؼϞσϧ ·ͱΊ ཧϞσϦϯά ղऍͱ൚Խ
ཧϞσϦϯά ཧϞσϦϯά ͱσʔλͷதʹ͋ΔߏΛࣜͰهड़͢Δ͜ͱ ྫ͑͜Μͳσʔλ͕༗Δ ͜ͷͱ͖όωAʹؔͯ͠ ʦόωͷ͞ʧʹ 0.2 x [͓Γͷॏ͞] +
3 ͱݱʹؔ͢Δࣜͷදݱ͕ಘΒΕΔɽ
ཧϞσϦϯά Ͳ͏ͬͨʁ όωAʹؔͯ͠ҎԼͷ࿈ཱํఔ͕ࣜͨͯΒΕΔ ͜ΕΛղ͚
ཧϞσϦϯά Կ͕͏Ε͍͠ʁ • ݱ࣮ͷͷߟʹֶͷςΫχοΫͰ͑ΒΕΔɽ • ײ͕ٴͳ͍ʹ͑Δ • ݫີ • ఆྔత
• ʮόωAͷํ͕৳ͼ͍͢ʯ
ཧϞσϦϯά ݫີʻʼײɼఆྔతʻʼఆੑత ʮؾԹ͕ߴ͍ͱδϝδϝ͢ΔͶ͐ʯ ͜Ε͜ΕͰॏཁɽ
ཽ ܭ ౷ ొ ཧ ֬ 14
ʮͱΓ͋͑ͣɺՄࢹԽ͠Αʁʯ 14
ཽ ܭ ౷ ొ ཧ ֬ 15
ʮࣜɺͨͯΐʁʯ 15
ཽ ܭ ౷ ొ ཧ ֬ 16
ʮσʔλΛೖ͠Αʁʯ 16
ཽ ܭ ౷ ొ ཧ ֬ 17
ʮύϥϝλܭࢉͰ͖ͨ͊ʂʂʯ 17
ཽ ܭ ౷ ొ ཧ ֬ 18
18 Click = CTR · Imp
ཽ ܭ ౷ ొ ཧ ֬ 19
19 pV = nRT
ཽ ܭ ౷ ొ ཧ ֬ 20
20
ཽ ܭ ౷ ొ ཧ ֬ 21
21 ࣗݾհͱ͝ΊΜͳ͍͞ ౷ܭϞσϦϯά ༧ଌͱ൚Խ ઢܗճؼϞσϧ ·ͱΊ ཧϞσϦϯά ղऍͱ൚Խ
౷ܭϞσϦϯά ౷ܭϞσϦϯά ͱ֬ʹجͮ͘ཧϞσϦϯάɽ ֬มΛؚΉϞσϧࣜΛ༻͍Δɽ ֬มͱϥϯμϜͳৼΔ͍ʹ؍ଌΛରԠ͚ΔΈͷ͜ͱɽ ཁ͢Δʹ ʮ ͕ग़ͨ−ʂʂʯʹʼ 1 ͬͯͳ۩߹ɽ
X : ! 2 ⌦ 7! X(!) 2 R
౷ܭϞσϦϯά ࣄͱߟͷରͱ͢ΔϥϯμϜͳৼΔ͍ͷ͋ͭ·Γɽ ͜Ε؍ଌ͕͇ΛԼճΔͱ͍͏ৼΔ͍ͷू·Γͷ͜ͱ ֶతͳఆٛ X : ! 2 ⌦ 7!
X(!) 2 R X < x [ X < x ] := X 1([ 1 , x )) = { ! 2 ⌦| X ( ! ) < x }
౷ܭϞσϦϯά ֬ͱ؍ଌͷཚࡶ͞ͷֶతදݱ ͜Εਖ਼نͰ͜Μͳײ͡ʹද͢ɽ ʮ֬มX͕ฏۉμɼඪ४ภࠩσͷਖ਼نʹै͏ʯͱಡΉɽ μσͳͲͷΛݸੑ͚ΔύϥϝλΛ ͱ͍͏ɽ X ⇠ N(µ,
2)
౷ܭϞσϦϯά ਪఆͱɼσʔλΛͱʹΛ༧͢Δ͜ͱ ʮΉΉʔʂ͜Ε֬0.5Ͱද͕ग़Δͷ͔͠Εͳ͍ʂʯ ʮͬͺ10ͷ1͘Β͍͔͠Εͳ͍ɽɽɽʯ ͜ͷਪఆͷʢͬͱʣΒ͠͞ͱݺΕ͍ͯΔ ද ཪ ද ཪ ཪ
ཪ ཪ ཪ ཪ ཪ ཪ ཪ
ཽ ܭ ౷ ొ ཧ ֬ 26
26 ࣗݾհͱ͝ΊΜͳ͍͞ ౷ܭϞσϦϯά ༧ଌͱ൚Խ ઢܗճؼϞσϧ ·ͱΊ ཧϞσϦϯά ղऍͱ൚Խ
ઢܗճؼϞσϧ ҎԼͷΑ͏ͳσʔλ͕༗Δɽ ͕ɼ࣮෩͕ਧ͍ͯͯਖ਼֬ʹܭଌग़དྷͯͳ͍ͬΆ͍ɽ ࠷ॳͱ͓ͳ͡ઢܗͷϞσϧࣜʹσʔλΛೖͯ͠ΈΔͱ
ઢܗճؼϞσϧ
ཽ ܭ ౷ ొ ཧ ֬ 29
ղ͚ͳ͌ɻɻɻ 29
ཽ ܭ ౷ ొ ཧ ֬ 30
ղͷͳ͌ɺ࿈ཱํఔࣜɻɻɻ 30
ཽ ܭ ౷ ొ ཧ ֬ 31
୳ͯ͠ɺݟ͔ͭΒͳ͌ͬͯίτɻɻɻ 31
ཽ ܭ ౷ ొ ཧ ֬ 32
͏ŵŧƄແཧɻ౷ܭ͠ΐɻɻɻ 32
ઢܗճؼϞσϧ ͜ͷϞσϧ؍ଌޡ͕ࠩߟྀ͞Ε͍ͯͳ͌ɻɻɻ ਖ਼نͷޡࠩԾఆ͢Δ y = ✓0 + ✓1x +✏ y
= ✓0 + ✓1x ✏ ⇠ N(0, 2)
ઢܗճؼϞσϧ ਖ਼نʹै͏ޡࠩΛԾఆͨ͠ϞσϧΛઢܗճؼϞσϧͱ͍͏ ✏ ⇠ N(0, 2) Y (✓0 + ✓1X)
⇠ N(0, 2) Y ⇠ N(✓0 + ✓1X, 2)
ཽ ܭ ౷ ొ ཧ ֬ 35
ਪఆ͠ΐɻɻɻ 35
ઢܗճؼϞσϧ ਖ਼نʹै͏ޡࠩΛԾఆͨ͠ϞσϧΛઢܗճؼϞσϧͱ͍͏ Ұ൪Β͍͠θͱσΛܭࢉ͢Δ ͜͜Ͱ Y ⇠ N(✓0 + ✓1X, 2)
L(✓1, ✓2, ) = Y i 1 p 2⇡ 2 e (yi µi)2 2 2 µi = ✓0 + ✓1xi
ઢܗճؼϞσϧ ରؔ θͷਪఆԼઢ෦Λ࠷খʹ͢Ε͍͍ࣄ͕Θ͔Δ ͜ΕΛ࠷খ2๏ͱ͍͏ɽ = 0
ઢܗճؼϞσϧ σʹؔ͢Δํఔࣜ ͜ΕΛղ͚ ͕ಘΒΕΔɽ͜Εඪຊࢄɽ @ @ log L ( ✓1,
✓2, ) = 0 2 = 1 n X i (yi µi)2
ઢܗճؼϞσϧ Rͩͱ؆୯ʹܭࢉͰ͖Δɽ
ཽ ܭ ౷ ొ ཧ ֬ 40
PythonͩͬͨΒɻɻɻ statsmodels / sklearn.linear_model.*** 40
ཽ ܭ ౷ ొ ཧ ֬ 41
41 ࣗݾհͱ͝ΊΜͳ͍͞ ౷ܭϞσϦϯά ղऍͱ൚Խ ઢܗճؼϞσϧ ·ͱΊ ཧϞσϦϯά
ઢܗճؼϞσϧ ղऍλεΫ ؍ଌ͞Εͨσʔλͷੑ࣭ΛௐΔɽ ੑผ༧ଌϞσϧ αΠτAΛݟͯΔͷஉੑ͕ଟ͍ɽ
ઢܗճؼϞσϧ ղऍλεΫ ͜ͷCPAʢ͋ͨΓίετʣࢪࡦͷྑ͞ͷධՁͱͯ͠༗ޮ Ͱɽɽɽ ʮ2ஹԁग़ͨ͠ΔΘ ɼ2ԯCVΖʯʹʼ͑ͬɾɾɾ ͪΖΜແཧ͕͋Δ CV = 1
CPA · Cost
ઢܗճؼϞσϧ ղऍλεΫ ͜ͷCPAʢ͋ͨΓίετʣࢪࡦͷྑ͞ͷධՁͱͯ͠༗ޮ Ͱɽɽɽ ʮ2ஹग़ͨ͠ΔΘʯ ʹʼ 2ԯCVʁʁʁ ͪΖΜແཧ͕͋Δ CV =
1 CPA · Cost y=x/CPA
ઢܗճؼϞσϧ ൚ԽλεΫ ະͷσʔλʹର͢Δ༧ଌੑೳࢸ্ओٛ • Neural Network • Gradient Boosting Decision
Tree • SVM with some kernel • Ridge/Lasso • Feature Hashing ౷ܭతͳͷΈͰಈ͍͍ͯͳ͍͕ଟ͍ Α͘Θ͔ΒΜ͕Կނ͔ͨΔ
ઢܗճؼϞσϧ ൚ԽλεΫ minimize: loss(label, Feature) Feature Label
ཽ ܭ ౷ ొ ཧ ֬ 47
47 ࣗݾհͱ͝ΊΜͳ͍͞ ౷ܭϞσϦϯά ղऍͱ൚Խ ઢܗճؼϞσϧ ·ͱΊ ཧϞσϦϯά
• ཧϞσϦϯάΛ༻͍Εݱ࣮ͷΛֶͷϊ ϋͰղܾͰ͖Δ • ౷ܭతͳςΫχοΫΛ͏͜ͱͰߋʹॊೈʹ • ൚ԽͱղऍϞσϧผͷςΫχοΫ ·ͱΊ
ੈా୩۠ࡏॅ H.M͞Μ ʮ࠷ॳʰ͜Μͳॻ੶Ͱඞཁͳ͕ࣝΈʹͭ͘ͳΜͯɾɾɾʱͱ͍͏ؾ࣋ͪ ͋Γɺ৴ٙͰ͜ͷຊΛखʹऔΓ·ͨ͠ɻ͍͟खʹͱͬͯݟΔͱShell ScriptSQLͷجૅͪΖΜɼPythonʹΑΔ࣮ફతͳΞϓϦέʔγϣϯͷ ࡞Γํ·Ͱஸೡʹղઆ͞Ε͍ͯͯ༧Ҏ্ͷϘϦϡʔϜͰͨ͠ɻͱ͘ʹۤख ͩͬͨ౷ܭϞσϦϯάטΈࡅ͍ͯॻ͔Ε͍ͯͯऔֻ͔ͬΓʹ࠷ߴͩͬͨ ͱࢥ͍·͢ɻ2000ԁऑͱ͍͏Ձֶ֨ੜʹخ͍͠Ͱ͢ɻࠓͰຖ൴ঁͱ ͤʹΒͯ͠ډ·͢ɻʯ ͨͳ͠ΎΜύΫͬͨ͝ΊΜ