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Rsqrd AI: Explaining ML Models w/ Geometric Intuition

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Rsqrd AI: Explaining ML Models w/ Geometric Intuition

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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**

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**

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Rsqrd AI: Explaining ML Models w/ Geometric Intuition

  1. 1. Explaining ML Models w/ Geometric Intuition 21 Aug 2019 at AI2 Leo Dirac
  2. 2. Outline • Motivating Example • Co-Linearity • Non-Linearity
  3. 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
  4. 4. M L B A L L
  5. 5. Input Outcome / Prediction Model
  6. 6. Player Data Great player? https://www.flickr.com/photos/clappstar/5813001871/ CC BY-NC-ND 2.0
  7. 7. Player Data: d features 0.99999 ∞ 0.00001 -∞ 0.5 0 Better Greatness
  8. 8. What to use for x? • How Fast? • How Strong? • Batting Average • Home runs • Balls caught Each player is a vector
  9. 9. ?
  10. 10. Feature Importance • Which features matter the most • Relatively straightforward for linear models • Intuitive Understandable vs Accurate
  11. 11. Two Problems
  12. 12. Co-Linearity
  13. 13. Get more data about speed! • 100m dash • 200m dash • Mile run • Height
  14. 14. Feature importance w/ co-linearity • What’s more important? • 100m time? • 100yd time? • 200m time?
  15. 15. Missing Data Missing Data
  16. 16. 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.
  17. 17. Feature importance w/ colinearity d features 2d possible combos Train them all!
  18. 18. 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
  19. 19. Non-Linearity
  20. 20. ?
  21. 21. Surrogate Explanation model Simple surrogate explanation model Full complex accurate model
  22. 22. S f
  23. 23. Local Surrogate Explanation model LIME: https://arxiv.org/abs/1602.04938
  24. 24. x Sx f
  25. 25. https://www.privacy-regulation.eu/en/r71.htm
  26. 26. Thanks! Me: twitter.com/leopd Jupyter Notebooks w/ Charts and Equations: github.com/leopd/explaining-colinear

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