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Semelhante a Explainable Machine Learning
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Explainable Machine Learning
- 2. Do we need to explanations?
- 3. Linear models to the rescue
- 4. But... What if we could train a Random Forest that seems to perform much better?
- 5. Random Forest Feature Importances
- 6. Decision Paths Prediction for: RM LSTAT NOX DIST 3.1 4.5 0.54 2.6 http://blog.datadive.net/interpreting-ra ndom-forests
- 7. treeinterpreter — Interpreting predictions with Decision Paths
- 8. treeinterpreter — Explaining difference between 2 datasets with Decision Paths
- 9. treeinterpreter — What about feature interactions?
- 10. treeinterpreter — Classification on the Iris dataset
- 11. treeinterpreter / Pivotal — Explanation on steroids — give me some dataviz
- 12. treeinterpreter / Pivotal — Contribution vs Feature Value (single Decision Tree)
- 13. treeinterpreter / Pivotal — Contribution vs Feature Value (Random Forest)
- 14. treeinterpreter / Pivotal — Contribution vs Feature Value (single Decision Tree)
- 15. treeinterpreter / Pivotal — Contribution vs Feature Value (Random Forest)
- 16. treeinterpreter / Pivotal — Classification Feature Contributions Violin Plot (for class “Infant”)
- 17. treeinterpreter / Pivotal — Classification Contributions to each class vs Feature Value
- 18. What about Boosted Trees? Instead of averaging contributions across trees we just have have to sum them. Available with: ● ELI5 e.g., XGBoost, LightGBM
- 19. ELI5 — XGBoost — Feature Importances (Titanic dataset)
- 20. ELI5 — XGBoost prediction — Titanic dataset
- 21. Model-agnostic explanations e.g., for non-tree based models
- 22. Lime ● Local approximations ● Model agnostic ● Can select a set of representative instances with explanations
- 24. Lime — creating superpixels to aid in image recognition
- 25. Lime — image recognition
- 26. Lime for NLP
- 27. More use cases ● Learn the right features (dogs vs wolves) ● Understand whether model overfits a particular feature ● Identifying data leakage ● Dataset shift (training data different than test data) ● Pneumonia/asthma case Amazon, Netflix
- 28. ● Not only useful for when things aren't working ● Different costs for making mistakes
- 29. References Interpreting Random Forests Random forest interpretation with scikit-learn Random forest interpretation – conditional feature contributions Interpreting Decision Trees and Random Forests XGBoost Decision Paths Explaining XGBoost predictions on the Titanic dataset “Why Should I Trust You?” Explaining the Predictions of Any Classifier