विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बहुपद समाश्रयण (Polynomial Regression)× | रिग्रेशन और स्मूथिंग स्प्लाइन्स× | |
|---|---|---|
| क्षेत्र≠ | सांख्यिकी | मशीन अधिगम |
| परिवार≠ | Regression model | Machine learning |
| उद्भव वर्ष≠ | 2012 | 1996 |
| प्रवर्तक≠ | Montgomery, Peck & Vining (textbook treatment); classical least squares | Spline regression literature; P-splines by Eilers & Marx |
| प्रकार≠ | Linear regression in transformed predictors | Piecewise-polynomial nonparametric regression |
| मौलिक स्रोत≠ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 | Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI ↗ |
| उपनाम≠ | polynomial least squares, curvilinear regression, Polinom Regresyonu | splines, cubic splines, natural splines, smoothing splines |
| संबंधित | 4 | 4 |
| सारांश≠ | 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. | 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. |
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