Comparer des méthodes

	Arbre de décision régularisé	Forêt Aléatoire Régularisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984	2012
Auteur d'origine	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Deng, H. & Runger, G.
Type	Supervised learning (regularized tree)	Regularized ensemble (penalized feature selection in trees)
Source fondatrice	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	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	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble
Apparentées	6	5
Résumé	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.	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.