What is feature importance and how is it computed?

Feature importance measures how much each feature contributes to model predictions.

Feature importance can be computed using impurity reduction (tree-based models), permutation importance, or model-specific weights. It helps interpret models and identify influential features. However, correlated features can distort importance scores.