Krahasoni metodat

	Regresioni logjistik gjysmë-mbikëqyrës	Regresioni logjistik (ML)
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	1995–2000	1958
Krijuesi	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)	Cox, D. R.
Lloji	Semi-supervised classifier	Probabilistic linear classifier
Burimi themelues	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Emërtime të tjera	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier	logit model, logit regression, binomial logistic regression, maximum entropy classifier
Të lidhura	5	5
Përmbledhja	Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.	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.