Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Локальная регрессия LOESS / LOWESS× | Регрессионные и сглаживающие сплайны× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1979 | 1996 |
| Автор метода≠ | William S. Cleveland | Spline regression literature; P-splines by Eilers & Marx |
| Тип≠ | Local nonparametric regression smoother | Piecewise-polynomial nonparametric regression |
| Основополагающий источник≠ | Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI ↗ | Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI ↗ |
| Другие названия≠ | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon | splines, cubic splines, natural splines, smoothing splines |
| Связанные≠ | 3 | 4 |
| Сводка≠ | LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots. | 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. |
| ScholarGateНабор данных ↗ |
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