Comparer des méthodes

	Arbre de décision ensembliste	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996–2000	2001
Auteur d'origine	Breiman, L.; Dietterich, T. G.	Breiman, L.
Type	Ensemble (multiple decision trees combined)	Ensemble (bagging of decision trees)
Source fondatrice	Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees)	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	6	4
Résumé	Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.	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.