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	Online Decision Tree	Online Learning
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	2000	1958–2000s
Upphovsperson	Domingos, P. & Hulten, G.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Typ	Incremental supervised classifier	Learning paradigm (sequential model update)
Ursprungskälla	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Alias	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	incremental learning, sequential learning, streaming learning, online machine learning
Närliggande	6	6
Sammanfattning	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 learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.