Сравнение методов

	Градиентный бустинг	Онлайн-обучение	Online Random Forest
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2001	1958–2000s	2009
Автор метода	Friedman, J. H.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Saffari, A. et al.
Тип	Ensemble (sequential boosting of decision trees)	Learning paradigm (sequential model update)	Incremental ensemble (streaming decision trees)
Основополагающий источник	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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.
Другие названия	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	incremental learning, sequential learning, streaming learning, online machine learning	ORF, streaming random forest, incremental random forest, adaptive random forest
Связанные	5	6	6
Сводка	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 learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	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.