Сравнение методов

	Online Random Forest	Случайный лес
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2009	2001
Автор метода	Saffari, A. et al.	Breiman, L.
Тип	Incremental ensemble (streaming decision trees)	Ensemble (bagging of decision trees)
Основополагающий источник	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	ORF, streaming random forest, incremental random forest, adaptive random forest	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	6	4
Сводка	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.