Methoden vergleichen

	Voting Ensemble	Bagging (Bootstrap Aggregating)	Boosting	Random Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning	Machine learning
Entstehungsjahr	1990s–2004	1996	1990–1997	2001
Urheber	Lam & Suen; Kuncheva, L. I. (systematic treatment)	Breiman, L.	Schapire, R. E.; Freund, Y.	Breiman, L.
Typ	Ensemble (combination of multiple classifiers by vote)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)	Ensemble (bagging of decision trees)
Wegweisende Quelle	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasnamen	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	5	5	6	4
Zusammenfassung	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.	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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.