Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Багатовимірні адаптивні сплайнові регресії (MARS)× | Регресійні та згладжувальні сплайни× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1991 | 1996 |
| Автор методу≠ | Jerome H. Friedman | Spline regression literature; P-splines by Eilers & Marx |
| Тип≠ | Adaptive piecewise-linear regression | Piecewise-polynomial nonparametric regression |
| Основоположне джерело≠ | Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67. DOI ↗ | Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI ↗ |
| Інші назви≠ | multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları | splines, cubic splines, natural splines, smoothing splines |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
|
|