Machine learning
回归与平滑样条
回归样条通过拟合在称为节点的一组点处平滑连接的分段多项式来模拟非线性关系。三次样条和自然样条是最常见的，而平滑样条则增加了一个粗糙度惩罚，可以自动平衡拟合与平滑度。样条是单变量非线性回归的标准灵活构建块，也是广义可加模型的基石。
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Method map
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来源
- Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI: 10.1214/ss/1038425655 ↗
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0
如何引用本页
ScholarGate. (2026, June 2). Regression and Smoothing Splines. ScholarGate. https://scholargate.app/zh/machine-learning/regression-splines
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 广义可加模型 (GAM)机器学习↔ compare
- LOESS / LOWESS局部回归机器学习↔ compare
- 多元自适应回归样条 (MARS)机器学习↔ compare
- 多项式回归统计学↔ compare