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
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
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
20260820_アウトカムが二値のデータに対するCausal Impact@LINEヤフー Data Science Share #2 / Causal Impact for Binary Outcomes
brainpadpr
3
1.4k
AI(人工知能)の過去・現在・未来 ~AIは人類を越えるのか~
tagtag
PRO
0
160
J-STAGE全文XML登載必須化について
xspa2012
0
1.5k
Inside the Mind of an LLM
baggiponte
0
340
JSAI2026企画セッションKS-14 インタビュー集『⼈⼯知能と哲学と四つの問い』が提起する⼈⼯知能のこれからの課題 趣旨説明 / JSAI2026 Special Session: A Collection of Interviews, “Artificial Intelligence, Philosophy, and Four Questions”
ykiyota
0
460
機械学習 - DBSCAN
trycycle
PRO
0
2.1k
Physical AIを支えるWeights & Biases
olachinkei
1
630
Note agro-climatique et prairies - Numéro 7
institudelelevage
PRO
0
270
HOLO: Homography-Guided Pose Estimator Network for Fine-Grained Visual Localization on SD Maps
tomoaki0705
0
150
「念のためのログ保存」を組織全体でやめるためのポリシーと仕組み作り
i2tsuki
4
380
Build your own LLM, Live, with MicroGPT
ianozsvald
0
150
AIPシンポジウム 2025年度 成果報告会 「因果推論チーム」
sshimizu2006
3
650
Featured
See All Featured
B2B Lead Gen: Tactics, Traps & Triumph
marketingsoph
0
240
Navigating Algorithm Shifts & AI Overviews - #SMXNext
aleyda
1
1.6k
The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs
inesmontani
PRO
3
3.7k
Design of three-dimensional binary manipulators for pick-and-place task avoiding obstacles (IECON2024)
konakalab
0
600
Designing for humans not robots
tammielis
254
26k
Highjacked: Video Game Concept Design
rkendrick25
PRO
1
460
Money Talks: Using Revenue to Get Sh*t Done
nikkihalliwell
0
500
GraphQLとの向き合い方2022年版
quramy
50
15k
Leveraging Curiosity to Care for An Aging Population
cassininazir
1
500
The Illustrated Children's Guide to Kubernetes
chrisshort
51
53k
Introduction to Domain-Driven Design and Collaborative software design
baasie
1
990
Designing for Performance
lara
611
70k
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