Сравнение методов

	Онлайн-дерево решений	Онлайн градиентный бустинг
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000	2011–2015
Автор метода	Domingos, P. & Hulten, G.	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.
Тип	Incremental supervised classifier	Online ensemble (sequential boosting on streaming data)
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.	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.
Другие названия	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent
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 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.