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.

For SHAP values, it should be the value of explainer.expected_value. However, it is recommended to pass in a SHAP Explanation object instead (shap_values is not necessary in this case).

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 …

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.

Nov 11, 2025 · 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 …

The y-axis is the SHAP value for that feature (stored in explanation.values), which represents how much knowing that feature’s value changes the output of the model for that sample’s prediction.