Sammenlign metoder

	Ensemble Decision Tree	Stemmeensemble
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1996–2000	1990s–2004
Ophavsperson	Breiman, L.; Dietterich, T. G.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Ensemble (multiple decision trees combined)	Ensemble (combination of multiple classifiers by vote)
Oprindelig kilde	Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Aliasser	decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees)	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Relaterede	6	5
Resumé	Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.	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.