Comparar métodos

	Regresión Lineal Múltiple	Regresión Lasso
Campo	Estadística	Aprendizaje automático
Familia	Regression model	Machine learning
Año de origen	1886	1996
Autor original	Francis Galton; formalized by Karl Pearson	Tibshirani, R.
Tipo	Parametric linear model	Regularized linear regression (L1 penalty)
Fuente seminal	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	MLR, OLS regression, multiple regression, linear regression with multiple predictors	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Relacionados	8	4
Resumen	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.	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.