Interpretable Machine Learning 2ed(2022) [Molnar] [9798411463330]

Description

Interpretable Machine Learning 2ed(2022) [Molnar] [9798411463330] Preface by the Author Introduction Story Time What Is Machine Learning? Terminology Interpretability Importance of Interpretability Taxonomy of Interpretability Methods Scope of Interpretability Evaluation of Interpretability Properties of Explanations Human-friendly Explanations Datasets Bike Rentals (Regression) YouTube Spam Comments (Text Classification) Risk Factors for Cervical Cancer (Classification) Interpretable Models Linear Regression Logistic Regression GLM, GAM and more Decision Tree Decision Rules RuleFit Other Interpretable Models Model-Agnostic Methods Example-Based Explanations Global Model-Agnostic Methods Partial Dependence Plot (PDP) Accumulated Local Effects (ALE) Plot Feature Interaction Functional Decompositon Permutation Feature Importance Global Surrogate Prototypes and Criticisms Local Model-Agnostic Methods Individual Conditional Expectation (ICE) Local Surrogate (LIME) Counterfactual Explanations Scoped Rules (Anchors) Shapley Values SHAP (SHapley Additive exPlanations) Neural Network Interpretation Learned Features Pixel Attribution (Saliency Maps) Detecting Concepts Adversarial Examples Influential Instances A Look into the Crystal Ball The Future of Machine Learning The Future of Interpretability Contribute to the Book Citing this Book Translations Acknowledgements