Sammenlign metoder

	Online Decision Tree	Online Gradient Boosting
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2000	2011–2015
Ophavsperson	Domingos, P. & Hulten, G.	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.
Type	Incremental supervised classifier	Online ensemble (sequential boosting on streaming data)
Oprindelig kilde	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.
Aliasser	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent
Relaterede	6	6
Resumé	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.