विधियों की तुलना करें

	सामान्यीकृत योगात्मक मॉडल (GAM)	बहुपद समाश्रयण (Polynomial Regression)
क्षेत्र	मशीन अधिगम	सांख्यिकी
परिवार	Machine learning	Regression model
उद्भव वर्ष	1986	2012
प्रवर्तक	Trevor Hastie & Robert Tibshirani	Montgomery, Peck & Vining (textbook treatment); classical least squares
प्रकार	Semi-parametric additive regression model	Linear regression in transformed predictors
मौलिक स्रोत	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
उपनाम	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	polynomial least squares, curvilinear regression, Polinom Regresyonu
संबंधित	4	4
सारांश	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.	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.