विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मल्टीवेरिएट एडप्टिव रिग्रेशन स्प्लाइन्स (MARS)× | बहुपद समाश्रयण (Polynomial Regression)× | |
|---|---|---|
| क्षेत्र≠ | मशीन अधिगम | सांख्यिकी |
| परिवार≠ | Machine learning | Regression model |
| उद्भव वर्ष≠ | 1991 | 2012 |
| प्रवर्तक≠ | Jerome H. Friedman | Montgomery, Peck & Vining (textbook treatment); classical least squares |
| प्रकार≠ | Adaptive piecewise-linear regression | Linear regression in transformed predictors |
| मौलिक स्रोत≠ | 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 |
| उपनाम≠ | multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları | polynomial least squares, curvilinear regression, Polinom Regresyonu |
| संबंधित | 4 | 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. | 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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