Comparer des méthodes

	Modèle additif généralisé (GAM)	Régression linéaire multiple
Domaine	Apprentissage automatique	Statistique
Famille	Machine learning	Regression model
Année d'origine	1986	1886
Auteur d'origine	Trevor Hastie & Robert Tibshirani	Francis Galton; formalized by Karl Pearson
Type	Semi-parametric additive regression model	Parametric linear model
Source fondatrice	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
Apparentées	4	8
Résumé	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.