Comparer des méthodes

	Gradient Boosting en ligne	Forêt aléatoire en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2011–2015	2009
Auteur d'origine	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Saffari, A. et al.
Type	Online ensemble (sequential boosting on streaming data)	Incremental ensemble (streaming decision trees)
Source fondatrice	Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216.	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	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	ORF, streaming random forest, incremental random forest, adaptive random forest
Apparentées	6	6
Résumé	Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible.	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.