Interpretable Machine Learning
A guide for making black box models explainable.
lime shapely shap interpretability explainability black-box tutorial article

After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.

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Authors
Interpretable Machine Learning researcher. Author of Interpretable Machine Learning Book: https://christophm.github.io/interpretable-ml-book/
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