विधियों की तुलना करें

	सामान्यीकृत रेखीय मॉडल (GLM)	सामान्यीकृत योगात्मक मॉडल (GAM)
क्षेत्र	सांख्यिकी	मशीन अधिगम
परिवार	Regression model	Machine learning
उद्भव वर्ष	1972	1986
प्रवर्तक	John A. Nelder & Robert W. M. Wedderburn	Trevor Hastie & Robert Tibshirani
प्रकार	Regression framework	Semi-parametric additive regression model
मौलिक स्रोत	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.
उपनाम	GLM, generalized regression, exponential family regression, link-function model	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
संबंधित	6	4
सारांश	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.