Comparer des méthodes

	Splines de régression et de lissage	Modèle additif généralisé (GAM)	Régression locale LOESS / LOWESS
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1996	1986	1979
Auteur d'origine	Spline regression literature; P-splines by Eilers & Marx	Trevor Hastie & Robert Tibshirani	William S. Cleveland
Type	Piecewise-polynomial nonparametric regression	Semi-parametric additive regression model	Local nonparametric regression smoother
Source fondatrice	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.
Alias	splines, cubic splines, natural splines, smoothing splines	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon
Apparentées	4	4	3
Résumé	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.	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.	LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.