All Notes Project Overview
Updated April 1, 2025

Shap

SHAP is a unified approach within Explainable AI (XAI) used to explain the output of any machine learning model. It leverages concepts from cooperative game theory, specifically Shapley values, to assign an importance value (the SHAP value) to each feature for a particular prediction.

Core Concept: Shapley Values

Shapley values provide a way to fairly distribute the “payout” (the model’s prediction deviation from the baseline) among the “players” (the input features). The SHAP value for a feature represents its marginal contribution to the prediction, averaged across all possible combinations of other features.

Key Features

  • Unified Framework: Connects various explanation methods (like LIME, DeepLIFT, Layer-Wise Relevance Propagation) under a single theoretical foundation.
  • Local Explanations: Provides detailed explanations for individual predictions, showing how much each feature contributed positively or negatively.
  • Global Explanations: Aggregating local SHAP values across many instances allows for understanding overall feature importance and model behavior (e.g., via summary plots like beeswarm or bar plots).
  • Consistency & Accuracy: Offers theoretical guarantees (like consistency) that some other methods lack.
  • Model Support: Optimized implementations exist for tree-based models (like XGBoost, LightGBM), deep learning models, and model-agnostic kernel-based approaches.

Use Cases

  • Explaining individual predictions in high-stakes applications (finance, healthcare).
  • Understanding global model behavior and identifying key drivers.
  • Debugging models by analyzing feature contributions.
  • Generating feature importance plots for reports and dashboards.

Connections

References