方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| LOESS / LOWESS局部回归× | 回归与平滑样条× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1979 | 1996 |
| 提出者≠ | William S. Cleveland | Spline regression literature; P-splines by Eilers & Marx |
| 类型≠ | Local nonparametric regression smoother | Piecewise-polynomial nonparametric regression |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon | splines, cubic splines, natural splines, smoothing splines |
| 相关≠ | 3 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|