Feature importance indicates which features contribute most to predictions. Higher values mean greater impact on model decisions.
Methods: Gini importance (Random Forest) • Permutation importance • SHAP values • Coefficient weights (Linear models)
Uses: Feature selection • Model interpretation • Remove irrelevant features • Understand predictions