Comparer des méthodes

	Boosting Régularisé	Forêt Aléatoire Régularisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001–2016	2012
Auteur d'origine	Friedman, J. H.; extended by Chen & Guestrin	Deng, H. & Runger, G.
Type	Regularized ensemble (boosting with shrinkage/penalty)	Regularized ensemble (penalized feature selection in trees)
Source fondatrice	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	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	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble
Apparentées	5	5
Résumé	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.	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.