In this talk, Leo Dirac talks about explaining ML models with geometric intuition. Presented 08/21/2019 **These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**

1. Explaining ML Models
w/ Geometric Intuition
21 Aug 2019 at AI2
Leo Dirac

2. Outline
• Motivating Example
• Co-Linearity
• Non-Linearity

3. Leo Dirac
• 6 Years at Amazon building ML Infrastructure
• Launched both AWS ML services
• Amazon ML (2015)
• Amazon SageMaker (2017)
• Pioneered lots of ML projects
• CNN Vision, Deep Recommendations, GPUs, AutoML

5. M L B A L L

6. Input
Outcome /
Prediction
Model

7. Player Data Great player?
https://www.flickr.com/photos/clappstar/5813001871/ CC BY-NC-ND 2.0

8. Player Data:
d features
0.99999 ∞
0.00001 -∞
0.5 0
Better
Greatness

9. What to use for x?
• How Fast?
• How Strong?
• Batting Average
• Home runs
• Balls caught
Each player is a vector

12. ?

13. Feature Importance
• Which features matter the most
• Relatively straightforward for linear models
• Intuitive
Understandable vs Accurate

14. Two Problems

15. Co-Linearity

16. Get more data about speed!
• 100m dash
• 200m dash
• Mile run
• Height

21. Feature importance w/ co-linearity
• What’s more important?
• 100m time?
• 100yd time?
• 200m time?

24. Missing Data
Missing Data

25. What happens if you take “100m time (s)” out
of the model?
• If you still have “100m time (min)” then absolutely nothing.
• If you still have “100yd time” then almost nothing.
• If you still have “200m time” then not much.
Therefore “100m time (s)” isn’t important.

26. Feature importance w/ colinearity
d features
2d possible combos
Train them all!

27. Shapley values
Average over all N possible feature
combinations cthat don’t include feature i
SHAP: https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
Prediction including
feature i
Prediction without
feature i

28. Non-Linearity

29. ?

30. Surrogate Explanation model
Simple
surrogate
explanation
model
Full
complex
accurate
model

31. S
f

32. Local Surrogate Explanation model
LIME: https://arxiv.org/abs/1602.04938

33. x
Sx
f

34. https://www.privacy-regulation.eu/en/r71.htm

35. Thanks!
Me: twitter.com/leopd
Jupyter Notebooks w/ Charts and Equations:
github.com/leopd/explaining-colinear