damionrashford Explain Skill
Generate model explanations with SHAP, LIME, integrated gradients, and permutation importance.
Quick start
# Auto-detect model type and run SHAP
uv run ${CLAUDE_SKILL_DIR}/scripts/shap_explain.py model.joblib data/test.csv
# Output: explanations/shap_summary.png
Methods by model type
| Model type | Recommended explainer |
|---|---|
| sklearn tree (RF, XGBoost) | SHAP TreeExplainer |
| sklearn linear | SHAP LinearExplainer |
| PyTorch/TF | SHAP DeepExplainer or captum |
| Any black-box | SHAP KernelExplainer (slow) or LIME |
Plots
shap.summary_plot()— global feature importance (beeswarm)shap.waterfall_plot()— single prediction breakdownshap.force_plot()— interactive prediction visualizationshap.dependence_plot()— feature interaction effectsPartialDependenceDisplay— marginal effect of one feature
When to use each
- SHAP: most accurate, works for any model, gold standard
- LIME: fast approximation, good for text and images
- Integrated gradients: neural nets only, attribution to input features
- Permutation importance: model-agnostic, measures drop in metric when feature shuffled
See references/explainability-guide.md for complete documentation and code examples.