Software Development
SHAP
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
- Publisher / source
- davila7/claude-code-templates (found on skills.sh)
- Upstream record
- davila7/claude-code-templates
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
fade1801bfeff92f58afaf828e711c7f40985b649bb23a6134b883d56f5811ab