Comparer des méthodes

	Modèle Linéaire Généralisé (GLM)	Modèle additif généralisé (GAM)
Domaine	Statistique	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	1972	1986
Auteur d'origine	John A. Nelder & Robert W. M. Wedderburn	Trevor Hastie & Robert Tibshirani
Type	Regression framework	Semi-parametric additive regression model
Source fondatrice	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.
Alias	GLM, generalized regression, exponential family regression, link-function model	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
Apparentées	6	4
Résumé	The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.	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.