Sammenlign metoder

	Semi-superviseret Naive Bayes	Logistisk regression
Fagområde	Maskinlæring	Forskningsstatistik
Familie	Machine learning	Process / pipeline
Oprindelsesår	2000	1958
Ophavsperson	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.	David Roxbee Cox
Type	Semi-supervised generative classifier	Method
Oprindelig kilde	Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Aliasser	SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier	logit model, binomial logistic regression, LR
Relaterede	4	3
Resumé	Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce.	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.