Compară metode

	Regresia prin metoda celor mai mici pătrate ordinare (OLS)	Regresia Lasso
Domeniu	Econometrie	Învățare automată
Familie	Regression model	Machine learning
Anul apariției	2019	1996
Autorul original	Wooldridge (textbook treatment); classical least squares	Tibshirani, R.
Tip	Linear regression	Regularized linear regression (L1 penalty)
Sursa seminală	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Denumiri alternative	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Înrudite	5	4
Rezumat	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).	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.