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	Zespołowy naiwny Bayes	Bagging (agregacja bootstrapowa)	Naiwny Klasyfikator Bayesowski
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2000s	1996	1997
Twórca	Various (Dietterich, T.G.; Webb, G.I.; others)	Breiman, L.	Mitchell, T. M. (textbook treatment)
Typ	Ensemble of probabilistic classifiers	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Probabilistic classifier (Bayes' theorem with conditional independence)
Źródło pierwotne	Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Inne nazwy	Bagged Naive Bayes, Boosted Naive Bayes, Naive Bayes ensemble, NB ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Pokrewne	6	5	4
Podsumowanie	Ensemble Naive Bayes trains multiple Naive Bayes classifiers — each exposed to a different view of the data through bagging, feature subsets, or boosting — and combines their probabilistic predictions by voting or probability averaging. The approach retains the speed and interpretability of individual Naive Bayes models while reducing variance and improving accuracy through ensemble aggregation.	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.