So sánh phương pháp

	Hồi quy Bình phương Tối thiểu Thông thường (OLS)	Ridge Regression
Lĩnh vực	Kinh tế lượng	Học máy
Họ	Regression model	Machine learning
Năm ra đời	2019	1970
Người khởi xướng	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
Loại	Linear regression	L2-regularized linear regression
Công trình gốc	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Tên gọi khác	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Liên quan	5	4
Tóm tắt	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.