Comparer des méthodes

	Régression logistique avec apprentissage actif	Naive Bayes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1994–2010	1997
Auteur d'origine	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	Mitchell, T. M. (textbook treatment)
Type	Active learning framework with logistic regression base learner	Probabilistic classifier (Bayes' theorem with conditional independence)
Source fondatrice	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Alias	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Apparentées	4	4
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.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.