Methoden vergleichen

	Ensemble Naive Bayes	Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2000s	1990–1997
Urheber	Various (Dietterich, T.G.; Webb, G.I.; others)	Schapire, R. E.; Freund, Y.
Typ	Ensemble of probabilistic classifiers	Sequential ensemble (iterative reweighting)
Wegweisende Quelle	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.
Aliasnamen	Bagged Naive Bayes, Boosted Naive Bayes, Naive Bayes ensemble, NB ensemble	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Verwandt	6	6
Zusammenfassung	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.