방법 비교

	Active Learning Logistic Regression	나이브 베이즈
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1994–2010	1997
창시자	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	Mitchell, T. M. (textbook treatment)
유형	Active learning framework with logistic regression base learner	Probabilistic classifier (Bayes' theorem with conditional independence)
원전	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
별칭	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
관련	4	4
요약	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.