Võrdle meetodeid

	Bayesi naiivne klassifikaator	Logistiline regressioon	Support Vector Machine (Klassifitseerimine)
Valdkond	Masinõpe	Uurimisstatistika	Masinõpe
Perekond	Machine learning	Process / pipeline	Machine learning
Tekkeaasta	1997	1958	1995
Looja	Mitchell, T. M. (textbook treatment)	David Roxbee Cox	Cortes, C. & Vapnik, V.
Tüüp	Probabilistic classifier (Bayes' theorem with conditional independence)	Method	Maximum-margin classifier (kernel method)
Algallikas	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.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Rööpnimetused	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	logit model, binomial logistic regression, LR	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Seotud	4	3	5
Kokkuvõte	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.