shap
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.
This page mirrors an upstream repository entry. It does not mean the skill is already part of the SCI Skills curated catalog.
- Raw path
- scientific-skills/shap
- Allowed tools
- -
- Repository version
- 2.31.0
- Synced at
- March 27, 2026
About this skill
About this skill
SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:
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