Comparar métodos

	Regresión por Mínimos Cuadrados Ordinarios (MCO)	Regresión Lasso
Campo	Econometría	Aprendizaje automático
Familia	Regression model	Machine learning
Año de origen	2019	1996
Autor original	Wooldridge (textbook treatment); classical least squares	Tibshirani, R.
Tipo	Linear regression	Regularized linear regression (L1 penalty)
Fuente seminal	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.
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Relacionados	5	4
Resumen	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.