Sammenlign metoder

	Online Decision Tree	Beslutningstræ
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2000	1984
Ophavsperson	Domingos, P. & Hulten, G.	Breiman, Friedman, Olshen & Stone
Type	Incremental supervised classifier	Recursive partitioning (if-then rules)
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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Aliasser	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Relaterede	6	5
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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.