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.
Installation
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
Voir la source ↗GuidesInstructions
Fonctions et usage
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.
Démarrage rapide
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
Guides ↗Exemples
Consultez les exemples du dépôt source.
Voir la source ↗Historique source
- Date d’indexation
- 2026-01-21
- Mise à jour de l’index
- 2026-02-28
- Branch
- main
Les dates concernent ce répertoire. Consultez le dépôt pour l’historique du code.
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