Individual skill

shap-model-explainability

Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.

skills/scientific-computing/shap-model-explainability/SKILL.md

Source commit 55c1bd3e863d11bf5a853d24bcdda10c88e6eddf

What was checked

Matches the observed repository HEAD

Yes

Scanner rules are current

Yes

Repository scan complete

No

Skill scan complete

No

Observed behavior

Network or broad shell

Last checked

Nov 5, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

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