পদ্ধতির তুলনা করুন

	এনসেম্বল লজিস্টিক রিগ্রেশন	Semi-supervised Logistic Regression
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1996–2000s	1995–2000
প্রবর্তক	Breiman, L. (bagging); broader ensemble literature	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
ধরন	Ensemble of logistic regression classifiers	Semi-supervised classifier
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	6	5
সারসংক্ষেপ	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.