Salīdzināt metodes

	Vispārīgais aditīvais modelis (GAM)	Lokālā regresija LOESS / LOWESS
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1986	1979
Autors	Trevor Hastie & Robert Tibshirani	William S. Cleveland
Tips	Semi-parametric additive regression model	Local nonparametric regression smoother
Pirmavots	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.
Citi nosaukumi	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon
Saistītās	4	3
Kopsavilkums	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.