方法对比

	在线决策树	在线梯度提升
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2000	2011–2015
提出者	Domingos, P. & Hulten, G.	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.
类型	Incremental supervised classifier	Online ensemble (sequential boosting on streaming data)
开创性文献	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.
别名	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent
相关	6	6
摘要	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.