विधियों की तुलना करें

	अर्ध-पर्यवेक्षित लॉजिस्टिक रिग्रेशन	लॉजिस्टिक रिग्रेशन (एमएल)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1995–2000	1958
प्रवर्तक	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)	Cox, D. R.
प्रकार	Semi-supervised classifier	Probabilistic linear classifier
मौलिक स्रोत	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.
उपनाम	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier	logit model, logit regression, binomial logistic regression, maximum entropy classifier
संबंधित	5	5
सारांश	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.