Machine learning
广义可加模型 (GAM)
广义可加模型（Generalized Additive Model, GAM）由 Trevor Hastie 和 Robert Tibshirani 于 1986 年提出，它通过用预测变量的平滑、数据驱动的函数替换每个线性项来扩展广义线性模型。这使得模型能够捕捉非线性关系，同时保留回归模型中逐项可解释的加性结构：每个预测变量贡献其自身的估计曲线，这些曲线（在连接函数尺度上）简单相加以预测响应变量。
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来源
- Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI: 10.1214/ss/1177013604 ↗
- Hastie, T. J., & Tibshirani, R. J. (1990). Generalized Additive Models. Chapman & Hall/CRC. ISBN: 978-0-412-34390-2
如何引用本页
ScholarGate. (2026, June 2). Generalized Additive Model (GAM). ScholarGate. https://scholargate.app/zh/machine-learning/generalized-additive-model
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