Comparer des méthodes

	Forêt Aléatoire Régularisée	Arbre de décision régularisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2012	1984
Auteur d'origine	Deng, H. & Runger, G.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Type	Regularized ensemble (penalized feature selection in trees)	Supervised learning (regularized tree)
Source fondatrice	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.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Alias	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Apparentées	5	6
Résumé	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.	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.