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Rsqrd AI: Exploring Machine Learning Model Predictions

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Rsqrd AI: Exploring Machine Learning Model Predictions

  1. 1. Age(years) Explaining Machine Learning Model Predictions Scott Lundberg
  2. 2. Why do we care so much about explainability in machine learning?
  3. 3. model 55% chance John will have repayment problems John, a bank customer No loan Why?! Why?! AI magic!
  4. 4. Interpretable Accurate Complex model ✘ ✔ Simple model ✔ ✘ Interpretable or accurate: choose one. 😀 ⚖ 💰 3
  5. 5. Complex models are inherently complex! But a single prediction involves only a small piece of that complexity. Input value Output value 5
  6. 6. 6 How did we get here? Base rate Prediction for John 20% 55%
  7. 7. 7 Base rate Work experience = 1 yr 20% 35% Day trader Open accounts = 1 70% Capital gains 90%55%
  8. 8. 8 The order matters! Work experience = 1 yr Day trader Nobel Prize in 2012 Lloyd Shapley
  9. 9. 9 SHapley Additive exPlanation (SHAP) values Age = 20 Day trader Shapley values result from averaging over all N! possible orderings. (NP-hard)
  10. 10. 10 Mortality risk model
  11. 11. Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Mortality risk model
  12. 12. Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Mortality risk model
  13. 13. Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Mortality risk model
  14. 14. Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Mortality risk model
  15. 15. Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Mortality risk model
  16. 16. Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Mortality risk model
  17. 17. Reveal rare high-magnitude mortality effects Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Conflates the prevalence of an effect with the magnitude of an effect Mortality risk model
  18. 18. Reveal rare high-magnitude mortality effects Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M)
  19. 19. Reveal rare high-magnitude mortality effects Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Rare high magnitude effects
  20. 20. Reveal rare high-magnitude mortality effects Global feature importance Local explanation summary(A) (log relative risk of mortality) Mortality model (F/M) Lots of ways to die young…Not many ways to live longer…
  21. 21. Dependence plots reveal the increased danger of early onset high blood pressure (C) SHAPvalueforsystolicbloodpressure withouttheageinteraction (logrelativeriskofmortality) Sys (B) (logrelativeriskofmortality) Systolic blood pressure (mmHg) Age(years) Kidney model = Vertical dispersion is driven by interaction effects 21
  22. 22. The varying risk of sex over a lifetime 22
  23. 23. 23 Model Monitoring
  24. 24. Model monitoring Time Training performance Test performance Can you find where we introduced the bug? 24
  25. 25. Model monitoring Now can you find where we introduced the bug? 25 False True
  26. 26. Model monitoring Time Transient electronic medical record Time 26 False True
  27. 27. Model monitoring Time Gradual change in atrial fibrillation ablation procedure durations Time 27 False True
  28. 28. Don’t take my word for it, try it yourself J github.com/slundberg/shap
  29. 29. github.com/slundberg/shap … Don’t take my word for it, try it yourself J
  30. 30. 30 Important questions to ask when using SHAP: 1. If you are using a model agnostic explainer, have you drawn enough samples? 2. What background population are you using to estimate the effect of a feature being “missing”? 3. What model output are you explaining? (log-odds, probability, rank-order, etc.) 4. Are you perturbing a model in ways that don’t make sense? (dealing with tightly correlated features)
  31. 31. Thanks!

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