เปรียบเทียบวิธี

	Lasso Regression	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	Ridge Regression
สาขาวิชา	การเรียนรู้ของเครื่อง	เศรษฐมิติ	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Regression model	Machine learning
ปีกำเนิด	1996	2019	1970
ผู้ริเริ่ม	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
ประเภท	Regularized linear regression (L1 penalty)	Linear regression	L2-regularized linear regression
แหล่งต้นตำรับ	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	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.
ชื่อเรียกอื่น	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
ที่เกี่ยวข้อง	4	5	4
สรุป	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.	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.