So sánh phương pháp

	Hồi quy Logistic	Naive Bayes
Lĩnh vực	Thống kê nghiên cứu	Học máy
Họ	Process / pipeline	Machine learning
Năm ra đời	1958	1997
Người khởi xướng	David Roxbee Cox	Mitchell, T. M. (textbook treatment)
Loại	Method	Probabilistic classifier (Bayes' theorem with conditional independence)
Công trình gốc	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Tên gọi khác	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Liên quan	3	4
Tóm tắt	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.	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.