So sánh phương pháp

	Hồi quy Lasso	Phương pháp Bề mặt Đáp ứng (RSM)	Ridge Regression
Lĩnh vực	Học máy	Thiết kế thí nghiệm	Học máy
Họ	Machine learning	Hypothesis test	Machine learning
Năm ra đời	1996	1951	1970
Người khởi xướng	Tibshirani, R.	George E. P. Box & K. B. Wilson	Hoerl, A.E. & Kennard, R.W.
Loại	Regularized linear regression (L1 penalty)	Second-order polynomial response surface model	L2-regularized linear regression
Công trình gốc	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Tên gọi khác	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	RSM, Central Composite Design, Box-Behnken Design, CCD	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Liên quan	4	7	4
Tóm tắt	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.	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.