An introduction to explainable AI with Shapley values — SHAP …
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 …
shap.Explainer — SHAP latest documentation
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 …
Image examples — SHAP latest documentation
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 …
shap.TreeExplainer — SHAP latest documentation
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 …
Be careful when interpreting predictive models in search of causal ...
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.
violin summary plot — SHAP latest documentation
The violin summary plot offers a compact representation of the distribution and variability of SHAP values for each feature. Individual violin plots are stacked by importance of the particular …
waterfall plot — SHAP latest documentation
This notebook is designed to demonstrate (and so document) how to use the shap.plots.waterfall function. It uses an XGBoost model trained on the classic UCI adult income dataset (which is …
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 an interaction term to see how it changes …
shap.DeepExplainer — SHAP latest documentation
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 …
shap.KernelExplainer — SHAP latest documentation
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.