Compute SHAP values for deep learning models that is based on connections between SHAP and the DeepLIFT algorithm.
Gradient explainer uses expected gradients, which merges ideas from integrated gradients, SHAP, and SmoothGrad into a single expection equation.
Kernel explainer is a method that uses a special weighted linear regression to compute the importance of each feature.
SBRL is a scalable Bayesian Rule List. It’s a generative estimator to build hierarchical interpretable decision lists.