SHAP (Shapley Additive Explanations) is a method used to explain the output of machine learning models by assigning each feature an importance value for a specific prediction. It is based on Shapley values, a concept from cooperative game theory that measures the contribution of each participant to a collective outcome. In machine learning, features are treated as “players” in a game where the prediction is the outcome. SHAP calculates how much each feature contributes to the difference between a model’s prediction for a specific instance and the average prediction across the dataset. For example, in a credit scoring model, SHAP could show that a applicant’s low income contributed -10% to their loan denial, while a poor credit history contributed +15%, helping developers understand why the model made that decision.
SHAP works by evaluating the impact of each feature through a systematic process. For a given prediction, it considers all possible combinations of features and calculates their marginal contributions. This involves testing how the prediction changes when a feature is included or excluded, averaged across all possible orderings of features. While this approach is theoretically sound, it can be computationally expensive for models with many features. To address this, SHAP offers optimized implementations like Kernel SHAP (model-agnostic approximation) and Tree SHAP (efficient calculation for tree-based models). For instance, in a healthcare model predicting patient risk, Tree SHAP might reveal that age and cholesterol levels are the top contributors to a high-risk prediction, while exercise habits have a smaller effect. SHAP values also adhere to properties like local accuracy, ensuring the sum of all feature contributions equals the difference between the prediction and the baseline (e.g., the dataset average).
Developers often use SHAP because it provides consistent, interpretable results across different model types. The Python shap library, for example, offers tools like summary plots (showing global feature importance) and force plots (visualizing individual predictions). A key advantage of SHAP over methods like LIME (Local Interpretable Model-agnostic Explanations) is its theoretical grounding—it guarantees that feature attributions are fairly distributed, avoiding contradictions in explanations. However, SHAP can be slow for large models or datasets, requiring trade-offs between accuracy and speed. Practical applications include debugging models (e.g., identifying unexpected feature dependencies), auditing for regulatory compliance, or communicating model behavior to non-technical stakeholders. By quantifying feature impacts, SHAP helps developers ensure models are transparent, reliable, and aligned with domain knowledge.