مقایسهٔ روش‌ها

	رگرسیون خطی اِنسِمبل (Ensemble Linear Regression)	رگرسیون خطی (یادگیری ماشین)
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	1996	1805–1809
پدیدآور	Breiman, L. (bagging framework)	Legendre, A.-M. & Gauss, C.F.
نوع	Ensemble of linear models	Supervised regression
منبع بنیادین	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7
نام‌های دیگر	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	ordinary least squares regression, OLS, least squares regression, multiple linear regression
مرتبط	6	5
خلاصه	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.	Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.