Comparer des méthodes

	Ensemble d'empilement explicable	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992 (stacking); 2010s–2020s (explainable extensions)	2001
Auteur d'origine	Wolpert, D. H. (stacking); XAI integration developed across the community	Friedman, J. H.
Type	Ensemble meta-learning with post-hoc or intrinsic interpretability	Ensemble (sequential boosting of decision trees)
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.