Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest)…
安装
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-shapnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-shap -a claude-code -gnpx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-shap -a codex -ggemini skills install https://github.com/AlterLab-IEU/AlterLab-Academic-Skills.git --path skills/data-science/alterlab-shapgh skill install AlterLab-IEU/AlterLab-Academic-Skills alterlab-shapgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skills /tmp/AlterLab-IEU__AlterLab-Academic-Skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-shap ~/.claude/skills/alterlab-shap # Claude Code
cp -R /tmp/AlterLab-IEU__AlterLab-Academic-Skills/skills/data-science/alterlab-shap ~/.agents/skills/alterlab-shap # Codex, Cursor, Gemini CLI, Copilot, OpenCode