Compară metode

	Regresie Liniară de Ansamblu	Regresia Liniară Regularizată
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1996	1970–2005
Autorul original	Breiman, L. (bagging framework)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Tip	Ensemble of linear models	Penalized linear model
Sursa seminală	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.
Denumiri alternative	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Înrudite	6	4
Rezumat	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.