Sammenlign metoder

	Ensemble Linear Regression	Stemmeensemble
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1996	1990s–2004
Ophavsperson	Breiman, L. (bagging framework)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Ensemble of linear models	Ensemble (combination of multiple classifiers by vote)
Oprindelig kilde	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Aliasser	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Relaterede	6	5
Resumé	Ensemble Linear Regression combines multiple ordinary least-squares models — each fitted on a different bootstrap sample or feature subset — and averages their predictions. The technique, grounded in Breiman's bagging framework (1996), reduces variance and improves predictive stability compared with a single linear regression fit, while retaining the interpretability of linear assumptions.	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.