Comparar métodos

	Modelo Aditivo Generalizado (GAM)	Regresión Lineal Múltiple
Campo	Aprendizaje automático	Estadística
Familia	Machine learning	Regression model
Año de origen	1986	1886
Autor original	Trevor Hastie & Robert Tibshirani	Francis Galton; formalized by Karl Pearson
Tipo	Semi-parametric additive regression model	Parametric linear model
Fuente seminal	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.
Alias	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Relacionados	4	8
Resumen	A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.	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.