Comparer des méthodes

	XGBoost Explicable	Gradient Boosting Explicable
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2016–2020	2017–2020
Auteur d'origine	Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)	Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)
Type	Interpretable ensemble (gradient-boosted trees + SHAP)	Ensemble + explainability layer
Source fondatrice	Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67.	Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.
Alias	XGBoost + SHAP, interpretable XGBoost, XAI-XGBoost, transparent gradient boosting	XGB with SHAP, interpretable gradient boosting, transparent gradient boosting, XAI gradient boosting
Apparentées	6	6
Résumé	Explainable XGBoost pairs the high predictive accuracy of XGBoost gradient-boosted trees with SHAP (SHapley Additive exPlanations) values to make each prediction fully auditable. The result is a model that matches or surpasses neural networks on tabular data while offering theoretically grounded, per-prediction feature attributions that satisfy both scientific transparency and regulatory demands.	Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.