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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.
shap-model-explainability is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # SHAP Model Explainability ## Overview SHAP (SHapley Additive exPlanations) is a unified framework for explaining machine learning model predictions using Shap. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding shap-model-explainability to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install FridrichMethod/awesome-skillsReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# SHAP Model Explainability ## Overview SHAP (SHapley Additive exPlanations) is a unified framework for explaining machine learning model predictions using Shapley values from coop