So sánh phương pháp

	Naive Bayes Trực tuyến	Cây Quyết định Trực tuyến
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2000s	2000
Người khởi xướng	Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)	Domingos, P. & Hulten, G.
Loại	Probabilistic classifier (online/incremental)	Incremental supervised classifier
Công trình gốc	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.	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.
Tên gọi khác	Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB	Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree
Liên quan	6	6
Tóm tắt	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.	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.