دانلود کتاب Interpretable Machine Learning

فهرست مطالب :

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