Сравнение методов

	Онлайн-дерево решений	Online Random Forest
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000	2009
Автор метода	Domingos, P. & Hulten, G.	Saffari, A. et al.
Тип	Incremental supervised classifier	Incremental ensemble (streaming decision trees)
Основополагающий источник	Domingos, P., & Hulten, G. (2000). Mining very fast data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). ACM.	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	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	ORF, streaming random forest, incremental random forest, adaptive random forest
Связанные	6	6
Сводка	An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time classification on potentially infinite data streams.	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.