Machine Learning

Interpretability

Explain models to humans: global + local understanding.

Practical Tools

Linear models: coefficients show direction and strength (after scaling).
Permutation importance: measure performance drop when a feature is shuffled.
Local explanations: SHAP/LIME style approaches for individual predictions.