방법 비교

	앙상블 선형 회귀	Voting Ensemble
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1996	1990s–2004
창시자	Breiman, L. (bagging framework)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
유형	Ensemble of linear models	Ensemble (combination of multiple classifiers by vote)
원전	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
별칭	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
관련	6	5
요약	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.