Σύγκριση μεθόδων

	Τοπική Παλινδρόμηση LOESS / LOWESS	Γενικευμένο Προσθετικό Μοντέλο (GAM)	Παλινδρόμηση και Εξομαλυντικές Σπλίνες
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	1979	1986	1996
Δημιουργός	William S. Cleveland	Trevor Hastie & Robert Tibshirani	Spline regression literature; P-splines by Eilers & Marx
Τύπος	Local nonparametric regression smoother	Semi-parametric additive regression model	Piecewise-polynomial nonparametric regression
Θεμελιώδης πηγή	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	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.
Εναλλακτικές ονομασίες	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	splines, cubic splines, natural splines, smoothing splines
Συναφείς	3	4	4
Σύνοψη	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.	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.