Comparar métodos

	Regresión lineal de conjunto	Regresión Ridge
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	1996	1970
Autor original	Breiman, L. (bagging framework)	Hoerl, A.E. & Kennard, R.W.
Tipo	Ensemble of linear models	L2-regularized linear regression
Fuente seminal	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Relacionados	6	4
Resumen	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.