विधियों की तुलना करें

	मल्टीवेरिएट एडप्टिव रिग्रेशन स्प्लाइन्स (MARS)	निर्णय वृक्ष	सामान्यीकृत योगात्मक मॉडल (GAM)
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1991	1984	1986
प्रवर्तक	Jerome H. Friedman	Breiman, Friedman, Olshen & Stone	Trevor Hastie & Robert Tibshirani
प्रकार	Adaptive piecewise-linear regression	Recursive partitioning (if-then rules)	Semi-parametric additive regression model
मौलिक स्रोत	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.
उपनाम	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
संबंधित	4	5	4
सारांश	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 Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.