Methoden vergleichen

	Boosting	Voting Ensemble
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1990–1997	1990s–2004
Urheber	Schapire, R. E.; Freund, Y.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Typ	Sequential ensemble (iterative reweighting)	Ensemble (combination of multiple classifiers by vote)
Wegweisende Quelle	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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Aliasnamen	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Verwandt	6	5
Zusammenfassung	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.