পদ্ধতির তুলনা করুন

	সাধারণীকৃত যোজন মডেল (GAM)	মাল্টিভেরিয়েট অ্যাডাপ্টিভ রিগ্রেশন স্প্লাইনস (MARS)	বহুপদী রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	পরিসংখ্যান
পরিবার	Machine learning	Machine learning	Regression model
উদ্ভবের বছর	1986	1991	2012
প্রবর্তক	Trevor Hastie & Robert Tibshirani	Jerome H. Friedman	Montgomery, Peck & Vining (textbook treatment); classical least squares
ধরন	Semi-parametric additive regression model	Adaptive piecewise-linear regression	Linear regression in transformed predictors
মৌলিক উৎস	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
অপর নাম	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	polynomial least squares, curvilinear regression, Polinom Regresyonu
সম্পর্কিত	4	4	4
সারসংক্ষেপ	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.	Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the data.	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.