Comparer des méthodes

	Ensemble d'empilement explicable	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992 (stacking); 2010s–2020s (explainable extensions)	2016
Auteur d'origine	Wolpert, D. H. (stacking); XAI integration developed across the community	Chen, T. & Guestrin, C.
Type	Ensemble meta-learning with post-hoc or intrinsic interpretability	Ensemble (gradient-boosted decision trees)
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	5
Résumé	Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.