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| Multivariate Adaptive Regression Splines (MARS)× | Polynomiale Regression× | |
|---|---|---|
| Fachgebiet≠ | Maschinelles Lernen | Statistik |
| Familie≠ | Machine learning | Regression model |
| Entstehungsjahr≠ | 1991 | 2012 |
| Urheber≠ | Jerome H. Friedman | Montgomery, Peck & Vining (textbook treatment); classical least squares |
| Typ≠ | Adaptive piecewise-linear regression | Linear regression in transformed predictors |
| Wegweisende Quelle≠ | Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67. DOI ↗ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 |
| Aliasnamen≠ | multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları | polynomial least squares, curvilinear regression, Polinom Regresyonu |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | 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. |
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