Comparer des méthodes

	Ensemble d'empilement régularisé	Forêt Aléatoire Régularisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1992–1996	2012
Auteur d'origine	Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)	Deng, H. & Runger, G.
Type	Ensemble (stacked generalization with regularized meta-learner)	Regularized ensemble (penalized feature selection in trees)
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8.
Alias	regularized stacked generalization, ridge stacking, lasso meta-learner ensemble, penalized stacking	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble
Apparentées	6	5
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 Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy.