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。
| 广义可加模型 (GAM)× | LOESS / LOWESS局部回归× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1986 | 1979 |
| 提出者≠ | Trevor Hastie & Robert Tibshirani | William S. Cleveland |
| 类型≠ | Semi-parametric additive regression model | Local nonparametric regression smoother |
| 开创性文献≠ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ | Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI ↗ |
| 别名 | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon |
| 相关≠ | 4 | 3 |
| 摘要≠ | A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response. | 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. |
| ScholarGate数据集 ↗ |
|
|