Interpretable Machine Learning

Interpretable machine learning Summary Preface by the Author 1 Introduction 1.1 Story Time 1.2 What Is Machine Learning? 1.3 Terminology 2 Interpretability 2.1 Importance of Interpretability 2.2 Taxonomy of Interpretability Methods 2.3 Scope of Interpretability 2.4 Evaluation of Interpretability 2.5 Properties of Explanations 2.6 Human-friendly Explanations 3 Datasets 3.1 Bike Rentals (Regression) 3.2 YouTube Spam Comments (Text Classification) 3.3 Risk Factors for Cervical Cancer (Classification) 4 Interpretable Models 4.1 Linear Regression 4.2 Logistic Regression 4.3 GLM, GAM and more 4.4 Decision Tree 4.5 Decision Rules 4.6 RuleFit 4.7 Other Interpretable Models 5 Model-Agnostic Methods 5.1 Partial Dependence Plot (PDP) 5.2 Individual Conditional Expectation (ICE) 5.3 Accumulated Local Effects (ALE) Plot 5.4 Feature Interaction 5.5 Permutation Feature Importance 5.6 Global Surrogate 5.7 Local Surrogate (LIME) 5.8 Scoped Rules (Anchors) 5.9 Shapley Values 5.10 SHAP (SHapley Additive exPlanations) 6 Example-Based Explanations 6.1 Counterfactual Explanations 6.2 Adversarial Examples 6.3 Prototypes and Criticisms 6.4 Influential Instances 7 Neural Network Interpretation 7.1 Learned Features 7.2 Pixel Attribution (Saliency Maps) 7.3 Detecting Concepts 8 A Look into the Crystal Ball 8.1 The Future of Machine Learning 8.2 The Future of Interpretability 9 Contribute to the Book 10 Citing this Book 11 Translations 12 Acknowledgements References R Packages Used for Examples Published with bookdown Interpretable Machine Learning A Guide for Making Black Box Models Explainable. — Lees op christophm.github.io/interpretable-ml-book/