विधियों की तुलना करें

	Naive Bayes	लॉजिस्टिक रिग्रेशन
क्षेत्र	मशीन अधिगम	अनुसंधान सांख्यिकी
परिवार	Machine learning	Process / pipeline
उद्भव वर्ष	1997	1958
प्रवर्तक	Mitchell, T. M. (textbook treatment)	David Roxbee Cox
प्रकार	Probabilistic classifier (Bayes' theorem with conditional independence)	Method
मौलिक स्रोत	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
उपनाम	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	logit model, binomial logistic regression, LR
संबंधित	4	3
सारांश	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.