Порівняння методів

	Ансамблева логістична регресія	Логістична регресія з напівкерованим навчанням
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	1996–2000s	1995–2000
Автор методу	Breiman, L. (bagging); broader ensemble literature	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Тип	Ensemble of logistic regression classifiers	Semi-supervised classifier
Основоположне джерело	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.
Інші назви	logistic regression ensemble, bagged logistic regression, aggregated logistic regression, logistic ensemble classifier	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier
Пов'язані	6	5
Підсумок	Ensemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by averaging or voting. The approach preserves logistic regression's probabilistic interpretability while reducing variance and improving predictive stability through aggregation.	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.