Comparer des méthodes

	Gradient Boosting	Forêt aléatoire en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	2009
Auteur d'origine	Friedman, J. H.	Saffari, A. et al.
Type	Ensemble (sequential boosting of decision trees)	Incremental ensemble (streaming decision trees)
Source fondatrice	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	ORF, streaming random forest, incremental random forest, adaptive random forest
Apparentées	5	6
Résumé	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.