API Reference 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. …

An introduction to explainable AI with Shapley values This is an introduction to explaining machine learning models with Shapley values. Shapley values are a widely used approach from cooperative …

Uses Shapley values to explain any machine learning model or python function. This is the primary explainer interface for the SHAP library. It takes any combination of a model and masker and returns …

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 …

1 SHAP Decision Plots 1.1 Load the dataset and train the model 1.2 Calculate SHAP values 2 Basic decision plot features 3 When is a decision plot helpful? 3.1 Show a large number of feature effects …

Basic SHAP Interaction Value Example in XGBoost This notebook shows how the SHAP interaction values for a very simple function are computed. We start with a simple linear function, and then add …

A sliceable set of parallel arrays representing a SHAP explanation. Notes The instance methods such as .max () return new Explanation objects with the operation applied. The class methods such as …

shap.DeepExplainer class shap.DeepExplainer(model, data, session=None, learning_phase_flags=None) Meant to approximate SHAP values for deep learning models. This is …

Tree SHAP is a fast and exact method to estimate SHAP values for tree models and ensembles of trees, under several different possible assumptions about feature dependence.

Mar 4, 2026 · Release notes To see the latest changes that are due on the next release, see v0.51.0…master. v0.51.0 Released on 2026-03-04 - GitHub - PyPI What's Changed Fixes fix: check …