Σύγκριση μεθόδων

	Λογιστική Παλινδρόμηση Ενεργητικής Μάθησης	Λογιστική Παλινδρόμηση
Πεδίο	Μηχανική Μάθηση	Ερευνητική Στατιστική
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	1994–2010	1958
Δημιουργός	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	David Roxbee Cox
Τύπος	Active learning framework with logistic regression base learner	Method
Θεμελιώδης πηγή	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier	logit model, binomial logistic regression, LR
Συναφείς	4	3
Σύνοψη	Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling.	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.