Comparer des méthodes

	Boosting	Forêt Aléatoire	Robust Boosting
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1990–1997	2001	1999–2001
Auteur d'origine	Schapire, R. E.; Freund, Y.	Breiman, L.	Freund, Y.; Mason, L. et al.
Type	Sequential ensemble (iterative reweighting)	Ensemble (bagging of decision trees)	Ensemble (robust sequential boosting)
Source fondatrice	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	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	6	4	6
Résumé	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual 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.	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.