A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models

In this tutorial, we implement SHAP workflows as a practical framework for interpreting machine learning models beyond basic feature-importance plots.

We start by training tree-based models and then compare different SHAP explainers, including Tree, Exact, Permutation, and Kernel methods, to understand how accuracy and runtime change across model-aware and model-agnostic approaches.