Confronta i metodi

	Gradient Boosting Online	Gradient Boosting	Online Random Forest
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2011–2015	2001	2009
Ideatore	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Friedman, J. H.	Saffari, A. et al.
Tipo	Online ensemble (sequential boosting on streaming data)	Ensemble (sequential boosting of decision trees)	Incremental ensemble (streaming decision trees)
Fonte seminale	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.	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	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	ORF, streaming random forest, incremental random forest, adaptive random forest
Correlati	6	5	6
Sintesi	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.	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.