Machine learningMachine learning
鲁棒梯度提升
鲁棒梯度提升（Robust Gradient Boosting）是一种梯度提升算法，它使用对异常值具有抵抗力的损失函数（最常见的是Huber损失或分位数（pinball）损失）进行训练，而不是使用平方误差损失。该变体由Friedman在其2001年的开创性论文中提出，它在保留梯度提升树全部预测能力的同时，能使预测结果受极端值或污染标签的扭曲程度大大降低。
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来源
- Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451 ↗
- Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI: 10.1214/aoms/1177703732 ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Gradient Boosting (Gradient Boosting with Robust Loss Functions). ScholarGate. https://scholargate.app/zh/machine-learning/robust-gradient-boosting
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.
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