Explore, Explain, and Debug aka Interpretable Machine Learning
1 like•359 views
The document discusses the importance of interpretability and explainability in machine learning models. It provides examples of how "black box" algorithms can have harmful and unsafe outcomes when used without understanding how they work. It advocates for techniques that allow humans to explore model predictions, understand how variables contribute to outcomes, and debug models when needed. These types of interpretable machine learning approaches will change how predictive models are developed and used.
Explore, Explain, and Debug aka Interpretable Machine Learning
- 1. Explore, Explain, and Debug aka Interpretable Machine Learning Przemysław Biecek
- 2. Why should we care?
- 8. • “You don’t see a lot of skepticism,” she says. “The algorithms are like shiny new toys that we can’t resist using. We trust them so much that we project meaning on to them.” • Ultimately algorithms, according to O’Neil, reinforce discrimination and widen inequality, “using people’s fear and trust of mathematics to prevent them from asking questions”. https://www.theguardian.com/books/2016/oct/27/cathy-oneil-weapons-of-math- destruction-algorithms-big-data !8 Cathy O'Neil: The era of blind faith in big data must end black boxes Why do we need explanations for complex models?
- 9. Right to explanation !9 Why do we need explanations for complex models?
- 11. DARPA cares Defense Advanced Research Projects Agency
- 14. We should too
- 15. Domain understanding Predictive modeling Validation and Justification EDA, transformations Linear models MSE, p-values Shift in our focus: Statistics
- 16. Domain understanding Predictive modeling Validation and Justification Domain understanding Predictive modeling Validation and Justification EDA, transformations Linear models MSE, p-values Simple EDA Lots of models + optimisation Test/train Cross Validation Shift in our focus: Machine Learning
- 17. Domain understanding Predictive modeling Validation and Justification Domain understanding Predictive modeling Validation and Justification Domain understanding Predictive modeling Validation and Justification EDA, transformations Linear models MSE, p-values Simple EDA Test/train Cross Validation Simple EDA AutoML XAI, Fairness, Ethics Validation and Justification Shift in our focus: Human Oriented ML? Lots of models + optimisation
- 18. What would you ask for?
- 20. https://kmichael08.github.io CHATBOT: What would you ask for?
- 21. https://kmichael08.github.io What If? Why? What happened to similar cases? CHATBOT: What would you ask for?
- 22. Why?
- 23. https://www.encyclopedia-titanica.org/ What are his odds of surviving? Random Forest prediction: 0.422
- 24. Input: 4 years old passenger from 1st class. Paid 72 for the ticket What is the contribution of each variable to the final odds? (model: Random Forest) iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models Alicja Gosiewska, Przemyslaw Biecek (2019) https://arxiv.org/abs/1903.11420v1 Random Forest prediction: 0.422
- 25. Additive attribution of model prediction via sequence of conditionings Added values of variable l in the sequence Final attributions
- 26. Conditional distributions, read from top to the bottom iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models Alicja Gosiewska, Przemyslaw Biecek (2019) https://arxiv.org/abs/1903.11420v1
- 27. Conditional distributions, read from top to the bottom
- 28. Break Down plots model agnostic, additive attributions
- 29. Order does matter! IME/EXPLAIN (Robnik 2008/2010), SHAP (Lundberg 2017), Break Down (our solution 2018)
- 30. Order does matter: SHAP is an average Break Down SHAP (SHapley Additive exPlanations) Lundberg (2017)
- 31. Order does matter: SHAP is an average Break Down SHAP (SHapley Additive exPlanations) Lundberg (2017)
- 32. Order does matter: use it to find interactions
- 33. What If?
- 34. What If? Input: 42 years old passenger from 1st class. Paid 72 for the ticket Logistic regression model predicts 0.32 probability of survival What would happen if….
- 35. What If? We cannot see all dimensions
- 36. Ceteris Paribus Profiles Individual Conditional Expectations
- 37. Champion - Challenger analysis
- 38. Interactive explanations for a better model - human interface
- 40. Meta models
- 41. Defaults (package defaults (Def.P) and optimal defaults (Def.O)), tunability of the hyperparameters with the package defaults (Tun.P) and our optimal defaults (Tun.O) as reference and tuning space quantiles (q0.05 and q0.95) for different parameters of the algorithms
- 42. Let’s focus on a single dataset: 334 For a selected class of models (here Random Forest) we can learn how the model performance depends on hyper-parameters
- 43. Let’s focus on a single dataset: 334 For a selected class of models (here Random Forest) we can learn how the model performance depends on hyper-parameters
- 44. FICO example for credit scoring
- 47. Performance for selected modeling methods red ones are the most interesting
- 48. Partial Dependency Plot for the most important feature
- 49. Partial Dependency Plot for the most important feature Break Down for a single decision made by GBM model with 10 000 trees
- 50. SAFE: Surrogate assisted feature extractions for ML models
- 51. Use a good black box model (i.e. trained with AutoML) and extract an interpretable model from it. AutoIML
- 52. AutoIML Use a good black box model (i.e. trained with AutoML) and extract an interpretable model from it. Preliminary results for the FICO data, xgboost is used as a surrogate to construct a logistic regression model.
- 53. Techniques for explanation and exploration will change the way how we do predictive models
- 54. MDP : : Model Development Process Data validation Feature selection Parameters tuning Problem formulation Crisp modelling Fine tuning Maintaining Data acquisition Model deployment Data cleaning Data exploration Sample selection Feature engineering Model selection Model validation Documentation Communication Data preparation Data understanding Model delivery Model assembly Model audit Model benchmarking Iterations P1 C1 C2 F1 F2 M1 M2 M3 time Techniques for explanation and exploration will change the way how we do predictive models
- 55. IML in R: DALEX, iml, mlr3vis(?), … IML in python: ELI5, skater, xai, SHAP, lime, … Other tools: H2O, …
- 56. An Introduction to Machine Learning Interpretability Navdeep Gill, Patrick Hall https://www.h2o.ai/oreilly-mli-booklet-2019/ Interpretable Machine Learning Christoph Molnar https://christophm.github.io/interpretable-ml-book/ Predictive Models: Explore, Explain, and Debug Przemyslaw Biecek and Tomasz Burzykowski https://pbiecek.github.io/PM_VEE/
- 57. Questions?