SHAP (SHapley Additive exPlanations)
SHAP is an open-source Python library that brings explainability to machine learning models by quantifying the contribution of each feature to individual predictions. Based on Shapley values from cooperative game theory, SHAP supports consistent, model-agnostic, and model-specific interpretation techniques.
Key Features
Local Interpretability: Explains individual predictions for any model.
Shapley Value Framework: Theoretically grounded approach to feature attribution.
Model Compatibility: Works with tree-based models (e.g., XGBoost, LightGBM), linear models, and deep learning frameworks.
Visualization Tools: Includes summary plots, dependence plots, force plots, and more.
Integration Ready: Easily used in Jupyter notebooks and compatible with common ML pipelines.
Example Use Cases
Understanding feature importance in complex black-box models
Explaining decisions in regulated domains like finance and healthcare
Debugging and auditing ML model predictions
Enhancing stakeholder trust in AI-driven systems