SHAP API patterns for model interpretability -- explainer selection, feature attribution, and visualization. Use when /ds:experiment needs per-prediction explanations, global feature importance, or interaction analysis. For built-in tree importance and permutation importance use scikit-learn; for coefficient-based interpretation use statsmodels.
How to install shap?
andikarachman/data-science-plugin/shap
$npx -y skills add andikarachman/data-science-plugin --skill shapInstalls into the current project.
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Run `npx skills use "https://github.com/andikarachman/data-science-plugin" --skill "andikarachman/data-science-plugin/shap"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/andikarachman/data-science-plugin" that are relevant to the current task. Run `npx skills add "https://github.com/andikarachman/data-science-plugin"` and select the relevant skills, then follow their instructions.
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