Comparer des méthodes

	Forêt Aléatoire	Robust Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1999–2001
Auteur d'origine	Breiman, L.	Freund, Y.; Mason, L. et al.
Type	Ensemble (bagging of decision trees)	Ensemble (robust sequential boosting)
Source fondatrice	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.
Alias	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
Apparentées	4	6
Résumé	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.	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.