featureimportance

Feature importance refers to methods that assign a numerical score to input features to indicate their usefulness for predicting a target variable in a machine learning model. These scores help users understand which features contribute most to the model’s predictions and can guide feature selection, model debugging, and domain interpretation.

Importances can be global, representing average contribution across the data, or local, describing a single prediction’s attribution to each feature. Global methods summarize feature effects over the dataset, while local methods explain individual predictions and can be aggregated if needed.

Common approaches fall into model-specific and model-agnostic categories. In tree-based models, feature importance is often reported as gain, split frequency (or weight), or coverage, reflecting how much a feature contributes to splits, how often it is used, or how many samples it affects. Model-agnostic techniques include permutation importance, which measures the decrease in a model’s performance when a feature’s values are shuffled, and surrogate-model explanations such as SHAP and LIME, which provide local attributions that can be aggregated for global interpretation.

Limitations include sensitivity to feature correlation, where related features can share or obscure importance, and to the scale or encoding of features. Some methods can be biased toward features with more categories or higher variance. Computational cost varies, with SHAP generally more intensive than permutation methods. Therefore, multiple methods and domain knowledge should inform interpretation.

Best practices emphasize reporting both global and local explanations, validating findings on held-out data, avoiding overinterpretation, and using feature importance to guide feature selection and engineering rather than to declare causal relationships.