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	Online Decision Tree	Semi-supervised Decision Tree
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	2000	2000s
Upphovsperson	Domingos, P. & Hulten, G.	Various (Levin & Shapiro; Zhu & Goldberg lineage)
Typ	Incremental supervised classifier	Semi-supervised classifier / regressor
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.	Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning.
Alias	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree
Närliggande	6	4
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.	A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.