A game theoretic approach to explain the output of any machine learning model
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).
Install
SHAP can be installed from either PyPI or conda-forge:
pip install shap or conda install -c conda-forge shap
Tree ensemble example (XGBoost/LightGBM/CatBoost/scikit-learn/pyspark models)
While SHAP can explain the output of any machine learning model, we have developed a high-speed exact