Comparer des méthodes

	Forêt Aléatoire Régularisée	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2012	2001
Auteur d'origine	Deng, H. & Runger, G.	Breiman, L.
Type	Regularized ensemble (penalized feature selection in trees)	Ensemble (bagging of decision trees)
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. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.