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

A tutorial shows how to build SHAP-based model interpretability workflows from end to end.

It compares TreeExplainer, KernelExplainer, Permutation, and Exact explainers for speed and accuracy.

The workflow adds correlation-aware masking, feature interactions, link functions, cohort analysis, and feature selection.

It also covers drift monitoring and custom explanations for black-box models, making SHAP more useful in production.