পদ্ধতির তুলনা করুন

	রিগ্রেশন এবং স্মুথিং স্প্লাইন	সাধারণীকৃত যোজন মডেল (GAM)	বহুপদী রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	পরিসংখ্যান
পরিবার	Machine learning	Machine learning	Regression model
উদ্ভবের বছর	1996	1986	2012
প্রবর্তক	Spline regression literature; P-splines by Eilers & Marx	Trevor Hastie & Robert Tibshirani	Montgomery, Peck & Vining (textbook treatment); classical least squares
ধরন	Piecewise-polynomial nonparametric regression	Semi-parametric additive regression model	Linear regression in transformed predictors
মৌলিক উৎস	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.	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
অপর নাম	splines, cubic splines, natural splines, smoothing splines	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	polynomial least squares, curvilinear regression, Polinom Regresyonu
সম্পর্কিত	4	4	4
সারসংক্ষেপ	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.	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.