Comparer des méthodes

	Forêt Aléatoire Régularisée	Arbre de décision
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2012	1984
Auteur d'origine	Deng, H. & Runger, G.	Breiman, Friedman, Olshen & Stone
Type	Regularized ensemble (penalized feature selection in trees)	Recursive partitioning (if-then rules)
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.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Alias	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Apparentées	5	5
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 Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.