featureattribution

Feature attribution is the process of explaining a model’s predictions by identifying how much each input feature contributed to a given outcome. It focuses on assigning an importance or contribution score to features for a specific prediction (local attribution) and can also summarize these contributions across many instances to describe global behavior.

Common approaches divide into model-agnostic and model-specific methods. Local surrogate methods, such as LIME, fit a simple interpretable model around a particular prediction to approximate the complex model’s behavior. SHAP (Shapley Additive Explanations) uses concepts from cooperative game theory to assign consistent, locally accurate attribution values that sum to the prediction. Integrated Gradients provide attribution for differentiable models by integrating gradients along a path from a reference input to the actual input. Occlusion and permutation-based methods assess attribution by observing prediction changes when features are masked or permuted.

Attribution can be local, providing explanations per instance, or global, summarizing feature importance across many predictions. Tree-based models often benefit from efficient TreeSHAP calculations, while deep learning models may rely on gradient-based or surrogate methods.

Applications span safety-critical and regulated domains such as healthcare and finance, model debugging and feature selection, and fairness analysis. However, attribution faces challenges: feature correlations can distort scores, explanations depend on the chosen method and reference point, and responsible use requires clear communication about limitations and uncertainty.

Best practices include using multiple attribution methods, reporting both local and global explanations when possible, checking for stability and consistency, and complementing quantitative attributions with domain expertise to avoid misinterpretation.