Linganisha mbinu

	Generalized Additive Model (GAM)	LOESS / LOWESS Usanifu wa Kurekebisha wa Kienyeji	Regressioni ya Polinomiali
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Takwimu
Familia	Machine learning	Machine learning	Regression model
Mwaka wa asili	1986	1979	2012
Mwanzilishi	Trevor Hastie & Robert Tibshirani	William S. Cleveland	Montgomery, Peck & Vining (textbook treatment); classical least squares
Aina	Semi-parametric additive regression model	Local nonparametric regression smoother	Linear regression in transformed predictors
Chanzo asilia	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.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
Majina mbadala	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	polynomial least squares, curvilinear regression, Polinom Regresyonu
Zinazohusiana	4	3	4
Muhtasari	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.	Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.