Bandingkan metode

	Regresi Linear Ensemble	Voting Ensemble
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1996	1990s–2004
Pencetus	Breiman, L. (bagging framework)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tipe	Ensemble of linear models	Ensemble (combination of multiple classifiers by vote)
Sumber perintis	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
Alias	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Terkait	6	5
Ringkasan	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.