Sammenlign metoder

	Ensemble Naive Bayes	Boosting	Naive Bayes
Fagfelt	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Opprinnelsesår	2000s	1990–1997	1997
Opphavsperson	Various (Dietterich, T.G.; Webb, G.I.; others)	Schapire, R. E.; Freund, Y.	Mitchell, T. M. (textbook treatment)
Type	Ensemble of probabilistic classifiers	Sequential ensemble (iterative reweighting)	Probabilistic classifier (Bayes' theorem with conditional independence)
Opprinnelig kilde	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Alias	Bagged Naive Bayes, Boosted Naive Bayes, Naive Bayes ensemble, NB ensemble	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Relaterte	6	6	4
Sammendrag	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.