مقایسهٔ روش‌ها

	رگرسیون لسو	رگرسیون حداقل مربعات معمولی (OLS)	رگرسیون ریج (Ridge Regression)
حوزه	یادگیری ماشین	اقتصادسنجی	یادگیری ماشین
خانواده	Machine learning	Regression model	Machine learning
سال پیدایش	1996	2019	1970
پدیدآور	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
نوع	Regularized linear regression (L1 penalty)	Linear regression	L2-regularized linear regression
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	4	5	4
خلاصه	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.