Comparer des méthodes

	Boosting	Forêt aléatoire en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	2009
Auteur d'origine	Schapire, R. E.; Freund, Y.	Saffari, A. et al.
Type	Sequential ensemble (iterative reweighting)	Incremental ensemble (streaming decision trees)
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.	Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	ORF, streaming random forest, incremental random forest, adaptive random forest
Apparentées	6	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.	Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.