Salīdzināt metodes

	Loģistikās regresijas aktīvā apguve	Logistiskā regresija
Nozare	Mašīnmācīšanās	Pētniecības statistika
Saime	Machine learning	Process / pipeline
Izcelsmes gads	1994–2010	1958
Autors	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	David Roxbee Cox
Tips	Active learning framework with logistic regression base learner	Method
Pirmavots	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.
Citi nosaukumi	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier	logit model, binomial logistic regression, LR
Saistītās	4	3
Kopsavilkums	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.