قارن الطرق

	انحدار لاسو	انحدار المربعات الصغرى العادية (OLS)	انحدار ريدج (Ridge Regression)
المجال	تعلم الآلة	الاقتصاد القياسي	تعلم الآلة
العائلة	Machine learning	Regression model	Machine learning
سنة النشأة	1996	2019	1970
صاحب الطريقة	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
النوع	Regularized linear regression (L1 penalty)	Linear regression	L2-regularized linear regression
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	4	5	4
الملخص	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.