Interpretable Machine Learning
Notes
Practical interpretability methods for black-box models: feature effects, SHAP, LIME, counterfactuals, and neural network explanations.
Practical interpretability methods for black-box models: feature effects, SHAP, LIME, counterfactuals, and neural network explanations.