Σύγκριση μεθόδων

	Online Naive Bayes	Λογιστική Παλινδρόμηση (ML)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2000s	1958
Δημιουργός	Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)	Cox, D. R.
Τύπος	Probabilistic classifier (online/incremental)	Probabilistic linear classifier
Θεμελιώδης πηγή	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB	logit model, logit regression, binomial logistic regression, maximum entropy classifier
Συναφείς	6	5
Σύνοψη	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.	Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.