Interpretable Machine Learning with Python

Table of Contents

1. Interpretation, Interpretability and Explainability; and why does it all matter?
2. Key Concepts of Interpretability
3. Interpretation Challenges
4. Fundamentals of Feature Importance and Impact
5. Global Model-Agnostic Interpretation Methods
6. Local Model-Agnostic Interpretation Methods
7. Anchor and Counterfactual Explanations
8. Visualizing Convolutional Neural Networks
9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
10. Feature Selection and Engineering for Interpretability
11. Bias Mitigation and Causal Inference Methods
12. Monotonic Constraints and Model Tuning for Interpretability
13. Adversarial Robustness
14. What's Next for Machine Learning Interpretability?