So sánh phương pháp

	Hồi quy Tuyến tính Tổng hợp	Ridge Regression
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1996	1970
Người khởi xướng	Breiman, L. (bagging framework)	Hoerl, A.E. & Kennard, R.W.
Loại	Ensemble of linear models	L2-regularized linear regression
Công trình gốc	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.
Tên gọi khác	bagged linear regression, aggregated linear regression, stacked linear models, bootstrap-aggregated OLS	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Liên quan	6	4
Tóm tắt	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.