Comparer des méthodes

	Empilement robuste ensembliste	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992 (stacking); robust variants 2000s–present	2001
Auteur d'origine	Wolpert, D. H. (stacking); robust extensions by multiple authors	Friedman, J. H.
Type	Ensemble (stacking with robust meta-learner)	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	robust stacking, robust stacked generalization, outlier-resistant stacking, stacking with robust meta-learner	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Apparentées	5	5
Résumé	Robust Stacking Ensemble extends classical stacked generalization by replacing the ordinary meta-learner with a robust estimator — such as a Huber-loss regressor, quantile regression, or a model trained on trimmed residuals — so that the ensemble's combination layer is resistant to outliers and noisy base-learner predictions. It improves predictive accuracy and reliability on real-world datasets with contaminated labels or heavy-tailed error distributions.	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.