We will take a practical hands-on approach, using the shap Python package to explain progressively more complex models. This is a living document, and serves as an introduction …

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.

This notebook is designed to demonstrate (and so document) how to use the shap.plots.beeswarm function. It uses an XGBoost model trained on the classic UCI adult …

This is the primary explainer interface for the SHAP library. It takes any combination of a model and masker and returns a callable subclass object that implements the particular estimation …

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 …

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 notebook shows how the SHAP interaction values for a very simple function are computed. We start with a simple linear function, and then add an interaction term to see how it changes …

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 …

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.

Meant to approximate SHAP values for deep learning models. This is an enhanced version of the DeepLIFT algorithm (Deep SHAP) where, similar to Kernel SHAP, we approximate the …