Comparer des méthodes

	Ensemble d'empilement régularisé	Gradient Boosting Régularisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992–1996	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Auteur d'origine	Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Type	Ensemble (stacked generalization with regularized meta-learner)	Regularized ensemble (additive tree model)
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 International Conference on Knowledge Discovery and Data Mining, 785–794.
Alias	regularized stacked generalization, ridge stacking, lasso meta-learner ensemble, penalized stacking	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Apparentées	6	6
Résumé	Regularized Stacking Ensemble is a two-level ensemble method in which predictions from multiple diverse base learners are combined by a regularized meta-learner — typically ridge regression, lasso, or elastic net — to suppress overfitting in the combination layer. Regularization ensures that the meta-learner assigns stable, well-calibrated weights to base model outputs rather than memorizing noise in the training fold predictions.	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.