Сравнение методов

	Online Naive Bayes	Онлайн-логистическая регрессия
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000s	1960s (perceptron); formalized for logistic loss ~2000s
Автор метода	Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.
Тип	Probabilistic classifier (online/incremental)	Incremental supervised classifier
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.	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.
Другие названия	Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier
Связанные	6	5
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.	Online Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible.