विधियों की तुलना करें

	बैगिंग (बूटस्ट्रैप एग्रीगेटिंग)	Naive Bayes
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1996	1997
प्रवर्तक	Breiman, L.	Mitchell, T. M. (textbook treatment)
प्रकार	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Probabilistic classifier (Bayes' theorem with conditional independence)
मौलिक स्रोत	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
उपनाम	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
संबंधित	5	4
सारांश	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.