So sánh phương pháp

	Bagging (Bootstrap Aggregating)	Naive Bayes
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1996	1997
Người khởi xướng	Breiman, L.	Mitchell, T. M. (textbook treatment)
Loại	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Probabilistic classifier (Bayes' theorem with conditional independence)
Công trình gốc	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Tên gọi khác	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Liên quan	5	4
Tóm tắt	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.