Сравнение методов

	Online Naive Bayes	Наивный Байес
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000s	1997
Автор метода	Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)	Mitchell, T. M. (textbook treatment)
Тип	Probabilistic classifier (online/incremental)	Probabilistic classifier (Bayes' theorem with conditional independence)
Основополагающий источник	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.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Другие названия	Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Связанные	6	4
Сводка	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.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.