Slide 1

Slide 1 text

Accumulated Local Effects 2021/10/30 95 R @ #TokyoR @dropout009

Slide 2

Slide 2 text

TVISION INSIGHTS Twitter: @dropout009 Speaker Deck: dropout009 Blog: https://dropout009.hatenablog.com/

Slide 3

Slide 3 text

No content

Slide 4

Slide 4 text

RF/GBDT/NN +

Slide 5

Slide 5 text

Partial Dependence

Slide 6

Slide 6 text

Partial Dependence PD PD! 𝑥" = 𝔼 & 𝑓 𝑥" , 𝑿∖" = * & 𝑓 𝑥" , 𝒙∖" 𝑝 𝒙∖" 𝑑𝒙∖" ! 𝑓(𝑿) 𝑋!

Slide 7

Slide 7 text

PD ' PD! 𝑥! = 1 𝑁 . "#$ % ! 𝑓(𝑥! , 𝒙",∖! )

Slide 8

Slide 8 text

PD R

Slide 9

Slide 9 text

PD 𝑌 = 𝑋! + 𝑋" " + 𝜖 𝑋! , 𝑋" ∼ Uniform 0, 1 𝜖 ∼ 𝒩(0, 0.01") CDF

Slide 10

Slide 10 text

tidymodels tidymodels rand_forest() Random Forest

Slide 11

Slide 11 text

= 0 PD PD

Slide 12

Slide 12 text

= 0.99 PD PD

Slide 13

Slide 13 text

PD ! 𝑓 𝑋$ , 𝑋( = 𝑋$ + 𝑋( ( PD! 𝑥! = 𝔼 9 𝑓 𝑥! , 𝑋" = 𝔼 𝑥! + 𝑋" " = ! # + 𝑥! PD" 𝑥" = 𝔼[ 9 𝑓 𝑋! , 𝑥" ] = 𝔼 𝑋! + 𝑥" " = ! " + 𝑥" " PD

Slide 14

Slide 14 text

PD

Slide 15

Slide 15 text

PD 𝑋! 𝑥! 𝑋" PD! 𝑥! = 𝔼 & 𝑓 𝑥! , 𝑋" = ∫ & 𝑓 𝑥!, 𝑥" 𝑝 𝑥" 𝑑𝑥" PD 𝑋!

Slide 16

Slide 16 text

4 PD 𝑋! 𝑥! 𝑋" (𝑥!,# , 𝑥!,! ) (𝑥$,# , 𝑥$,! ) (𝑥%,# , 𝑥%,! ) 𝑥#,# , 𝑥#,!

Slide 17

Slide 17 text

𝑋! 𝑥! 𝑋" 𝑥#,# , 𝑥#,! (𝑥!,# , 𝑥!,! ) (𝑥$,# , 𝑥$,! ) - PD! 𝑥! = ! ' & 𝑓 𝑥!, 𝑥!," + ! ' & 𝑓 𝑥!, 𝑥"," + ! ' & 𝑓 𝑥! , 𝑥(," + ! ' & 𝑓 𝑥! , 𝑥'," (𝑥%,# , 𝑥%,! ) ) 𝑓(𝑥# , 𝑥!,! ) ) 𝑓(𝑥# , 𝑥!,! ) ) 𝑓(𝑥# , 𝑥%,! ) ) 𝑓(𝑥# , 𝑥#,! ) PD

Slide 18

Slide 18 text

Conditional Dependence (Marginal Plot)

Slide 19

Slide 19 text

PD 𝑋! 𝑥! 𝑋" PD! 𝑥! = 𝔼 & 𝑓 𝑥! , 𝑋" = ∫ & 𝑓 𝑥!, 𝑥" 𝑝 𝑥" 𝑑𝑥" PD 𝑋!

Slide 20

Slide 20 text

築 𝑋! 𝑥! 𝑋" PD! 𝑥! = 𝔼 & 𝑓 𝑥! , 𝑋" = ∫ & 𝑓 𝑥!, 𝑥" 𝑝 𝑥" 𝑑𝑥" CD! 𝑥! = 𝔼 & 𝑓 𝑥! , 𝑋" ∣ 𝑋! = 𝑥! = ∫ & 𝑓 𝑥!, 𝑥" 𝑝 𝑥" ∣ 𝑥! 𝑑𝑥"

Slide 21

Slide 21 text

Conditional Dependence 𝑋! 𝑥! (") 𝑋" 𝑥! (() 𝑥! (') 𝑥! (+) 𝑥! (,) 𝑥! (!)

Slide 22

Slide 22 text

CD * 𝑓( 𝑥" ($%&) + 𝑥" ($) 2 , 𝒙(,∖" )

Slide 23

Slide 23 text

CD CD

Slide 24

Slide 24 text

CD 𝑓 𝑋! , 𝑋" = 𝑋! + 𝑋" " 𝑋! = 𝑋" CD! 𝑥! = 𝔼 & 𝑓 𝑥!, 𝑋" ∣ 𝑋! = 𝑥! = 𝔼 𝑥! + 𝑋" " ∣ 𝑋! = 𝑥! = 𝑥! + 𝔼 𝑋" " ∣ 𝑋! = 𝑥! = 𝑥! + 𝑥! " CD" 𝑥" = 𝔼 & 𝑓 𝑋!, 𝑥" ∣ 𝑋" = 𝑥" = 𝔼 𝑋! + 𝑥" " ∣ 𝑋" = 𝑥" = 𝔼 𝑋! ∣ 𝑋" = 𝑥" + 𝑥" " = 𝑥" + 𝑥" " CD 𝑋! 𝑋"

Slide 25

Slide 25 text

Accumulated Local Effects

Slide 26

Slide 26 text

ALE CD 𝑋! 𝑥! (") 𝑋" 𝑥! (() 𝑥! (') 𝑥! (+) 𝑥! (,) 𝑥! (!) 𝑥#,# , 𝑥#,! 𝑥!,# , 𝑥!,! 𝑥$,# , 𝑥$,!

Slide 27

Slide 27 text

ALE Local Effect Accumulate 𝑋! 𝑥! (") 𝑋" 𝑥! (() 𝑥! (') 𝑥! (+) 𝑥! (,) 𝑥! (!) ) 𝑓 𝑥# ($), 𝑥#,! ) 𝑓 𝑥# ($) , 𝑥!,! ) 𝑓 𝑥# ($), 𝑥!,! ) 𝑓 𝑥# (%), 𝑥#,! ) 𝑓 𝑥# (%), 𝑥!,! ) 𝑓 𝑥# (%), 𝑥!,! Local Effect 1 3 ) 𝑓 𝑥# (%), 𝑥#,! − ) 𝑓 𝑥# ($), 𝑥#,! + 1 3 ) 𝑓 𝑥# (%), 𝑥!,! − ) 𝑓 𝑥# ($), 𝑥!,! + 1 3 ) 𝑓 𝑥# (%), 𝑥$,! − ) 𝑓 𝑥# ($), 𝑥$,! Local Effect

Slide 28

Slide 28 text

ALE Local Effect Local Effect

Slide 29

Slide 29 text

ALE ALE

Slide 30

Slide 30 text

ALE 𝑓 𝑋! , 𝑋" = 𝑋! + 𝑋" " 𝑖 𝑋! [𝑥! (12!), 𝑥! (1)) Local Effect & 𝑓 𝑥! (1), 𝑥3," − & 𝑓 𝑥! 12! , 𝑥3," = 𝑥! 1 + 𝑥3," " − 𝑥! 12! + 𝑥3," " = 𝑥! 1 − 𝑥! 12! 𝑋" 𝑋! [𝑥! (12!), 𝑥! (1))

Slide 31

Slide 31 text

ALE ALE Local Effect 𝑋4 𝜕 * 𝑓(𝑋", 𝑿∖") 𝜕𝑋" 𝔼 𝜕 * 𝑓(𝑋", 𝑿∖") 𝜕𝑋" ∣ 𝑋" = 𝑧" ALE" 𝑥" = ; + ! (#$%) +! 𝔼 𝜕 * 𝑓(𝑋", 𝑿∖") 𝜕𝑋" ∣ 𝑋" = 𝑧" 𝑑𝑧" 𝑋4 = 𝑧4 Local Effect Local Effect 𝑥4 (567) ALE

Slide 32

Slide 32 text

ALE ! 𝑓 𝑋$ + 𝑋( ( = 𝑋$ + 𝑋( ( ALE ALE! 𝑥! = 9 8 9! 𝔼 𝜕(𝑋! + 𝑋" ") 𝜕𝑋! ∣ 𝑋! = 𝑧! 𝑑𝑧! = 9 8 9! 𝔼 1 ∣ 𝑋! = 𝑧! 𝑑𝑧! = 9 8 9! 1𝑑𝑧! = 𝑥! ALE" 𝑥" = 9 8 9" 𝔼 𝜕(𝑋! + 𝑋" ") 𝜕𝑋" ∣ 𝑋" = 𝑧" 𝑑𝑧" = 9 8 9" 𝔼 2𝑋" ∣ 𝑋" = 𝑧" 𝑑𝑧" = 9 8 9" 2𝑧"𝑑𝑧" = 𝑥 " "

Slide 33

Slide 33 text

No content

Slide 34

Slide 34 text

• • Partial Dependence ⾒ • Accumulated Local Effects ⾒ • ALE

Slide 35

Slide 35 text

• Friedman, Jerome H. "Greedy function approximation: a gradient boosting machine." Annals of statistics (2001): 1189-1232. • Hooker, Giles, and Lucas Mentch. "Please Stop Permuting Features: An Explanation and Alternatives." arXiv preprint arXiv:1905.03151 (2019). • Apley, Daniel W., and Jingyu Zhu. "Visualizing the effects of predictor variables in black box supervised learning models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82.4 (2020): 1059- 1086. • Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable." (2019). https://christophm.github.io/interpretable-ml-book/. • Biecek, Przemyslaw and Tomasz Burzykowski. "Explanatory Model Analysis. Chapman and Hall/CRC (2021). https://pbiecek.github.io/ema/. • . . . (2021). https://is.gd/nkYPPG

Slide 36

Slide 36 text

R