shap-model-explainability
Pass
Audited by Gen Agent Trust Hub on Apr 28, 2026
Risk Level: SAFEEXTERNAL_DOWNLOADS
Full Analysis
- [SAFE]: The skill provides documentation and code examples for using the SHAP library for machine learning interpretability. All provided instructions and code snippets are consistent with standard data science workflows and educational content.
- [EXTERNAL_DOWNLOADS]: Mentions standard Python package installations including shap, matplotlib, xgboost, lightgbm, tensorflow, torch, scikit-learn, joblib, and mlflow from the official PyPI registry. These are well-known libraries in the machine learning and data science community.
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