What is model interpretability in supervised learning?

Model interpretability refers to understanding how and why a model makes predictions.

Interpretability helps explain model decisions to humans. It can be global (overall feature influence) or local (individual prediction explanation). Interpretable models include linear regression and decision trees, while complex models like neural networks require tools like SHAP or LIME for explanation.