পদ্ধতির তুলনা করুন

	মাল্টিভেরিয়েট অ্যাডাপ্টিভ রিগ্রেশন স্প্লাইনস (MARS)	সাধারণীকৃত যোজন মডেল (GAM)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1991	1986
প্রবর্তক	Jerome H. Friedman	Trevor Hastie & Robert Tibshirani
ধরন	Adaptive piecewise-linear regression	Semi-parametric additive regression model
মৌলিক উৎস	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.
অপর নাম	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
সম্পর্কিত	4	4
সারসংক্ষেপ	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.	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.