পদ্ধতির তুলনা করুন

	অনলাইন ডিসিশন ট্রি	আধা-পর্যবেক্ষিত সিদ্ধান্ত বৃক্ষ
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000	2000s
প্রবর্তক	Domingos, P. & Hulten, G.	Various (Levin & Shapiro; Zhu & Goldberg lineage)
ধরন	Incremental supervised classifier	Semi-supervised classifier / regressor
মৌলিক উৎস	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.
অপর নাম	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree
সম্পর্কিত	6	4
সারসংক্ষেপ	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.