Comparer des méthodes

	Régression logistique avec apprentissage actif	Régression logistique
Domaine	Apprentissage automatique	Statistiques de recherche
Famille	Machine learning	Process / pipeline
Année d'origine	1994–2010	1958
Auteur d'origine	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	David Roxbee Cox
Type	Active learning framework with logistic regression base learner	Method
Source fondatrice	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.
Alias	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier	logit model, binomial logistic regression, LR
Apparentées	4	3
Résumé	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.