विधियों की तुलना करें

	एन्सेम्बल लीनियर रिग्रेशन	नियमित रैखिक प्रतिगमन
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1996	1970–2005
प्रवर्तक	Breiman, L. (bagging framework)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
प्रकार	Ensemble of linear models	Penalized linear model
मौलिक स्रोत	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
उपनाम	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
संबंधित	6	4
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.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.