This page contains the API reference for public objects and functions in SHAP. There are also example notebooks available that demonstrate how to use the API of each object/function.

Example of loading a custom tree model into SHAP Explaining a simple OR function Explaining the Loss of a Tree Model Fitting a Linear Simulation with XGBoost Force Plot Colors Front page example …

SHAP Decision Plots SHAP decision plots show how complex models arrive at their predictions (i.e., how models make decisions). This notebook illustrates decision plot features and use cases with …

Image examples These examples explain machine learning models applied to image data. They are all generated from Jupyter notebooks available on GitHub. Image classification Examples using …

Mar 4, 2026 · This release incorporates many changes that were originally contributed by the SHAP community via @dsgibbons 's Community Fork, which has now been merged into the main shap …

Uses Tree SHAP algorithms to explain the output of ensemble tree models. Tree SHAP is a fast and exact method to estimate SHAP values for tree models and ensembles of trees, under several …

SHAP and other interpretability tools can be useful for causal inference, and SHAP is integrated into many causal inference packages, but those use cases are explicitly causal in nature.

By using SHAP (a popular explainable AI tool) we can decompose measures of fairness and allocate responsibility for any observed disparity among each of the model’s input features.

Uses the Kernel SHAP method to explain the output of any function. Kernel SHAP is a method that uses a special weighted linear regression to compute the importance of each feature.

The documentation is hosted at shap.readthedocs.io. If you have modified the docstrings or notebooks, please also check that the changes are are rendered properly in the generated HTML files.