Compară metode

	Învățare activă K-vecini apropiați	Regresie Logistică cu Învățare Activă
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1951–2010	1994–2010
Autorul original	Settles, B. (active learning framework); Fix & Hodges (KNN base)	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)
Tip	Active learning with KNN base learner	Active learning framework with logistic regression base learner
Sursa seminală	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.
Denumiri alternative	AL-KNN, active KNN, query-based nearest neighbor learning, uncertainty-sampling KNN	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier
Înrudite	4	4
Rezumat	Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far fewer labeled examples than fully supervised KNN on tabular data.	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.