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	एन्सेम्बल लीनियर रिग्रेशन	मतदान एनसेंबल
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1996	1990s–2004
प्रवर्तक	Breiman, L. (bagging framework)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
प्रकार	Ensemble of linear models	Ensemble (combination of multiple classifiers by vote)
मौलिक स्रोत	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
उपनाम	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
संबंधित	6	5
सारांश	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.