A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models
This tutorial provides a hands-on framework for implementing SHAP explainability workflows, covering training tree-based models and comparing Tree, Exact, Permutation, and Kernel explainers. It explores masker effects on correlated features, interaction values, link functions, Owen values, cohort comparisons, feature selection, drift detection, and custom black-box explanations, all runnable in Google Colab.
A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models
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