Confronta i metodi

	Modello Additivo Generalizzato (GAM)	Regression and Smoothing Splines
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	1986	1996
Ideatore	Trevor Hastie & Robert Tibshirani	Spline regression literature; P-splines by Eilers & Marx
Tipo	Semi-parametric additive regression model	Piecewise-polynomial nonparametric regression
Fonte seminale	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.
Alias	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	splines, cubic splines, natural splines, smoothing splines
Correlati	4	4
Sintesi	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.	Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models.