পদ্ধতির তুলনা করুন

	অনলাইন ডিসিশন ট্রি	Online Naive Bayes
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000	2000s
প্রবর্তক	Domingos, P. & Hulten, G.	Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)
ধরন	Incremental supervised classifier	Probabilistic classifier (online/incremental)
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.	Domingos, P. & Hulten, G. (2000). Mining high-speed data streams. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71–80. ACM.
অপর নাম	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree	Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB
সম্পর্কিত	6	6
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.	Online Naive Bayes is an incremental adaptation of the classical Naive Bayes classifier that updates its class-conditional statistics one observation (or one mini-batch) at a time, making it well suited to data streams, very large datasets that cannot be held in memory, and settings where the model must adapt continuously as new labeled examples arrive.