Feature Importance

A measure of how much each feature contributes to model predictions. For tree models (Random Forest, XGBoost): typically measures the reduction in impurity from splits on that feature. For linear models: the magnitude of the coefficient. For neural networks: usually computed via SHAP or permutation importance. Useful for debugging, model compression, and explaining predictions to stakeholders.

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