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鲁棒XGBoost

鲁棒XGBoost将XGBoost的可扩展梯度提升框架与鲁棒损失函数（主要是Huber损失或其变体）相结合，生成一个能够抵抗异常值扭曲影响的梯度提升树集成模型。通过用一个对大残差影响进行降权的损失函数替换平方误差目标函数，该模型即使在训练数据包含极端值或标签噪声时，也能对连续目标变量提供可靠的预测。

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鲁棒XGBoost
梯度提升（Gradient Boosting）鲁棒梯度提升鲁棒LightGBM稳健线性回归鲁棒随机森林XGBoost

来源

  1. Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI: 10.1145/2939672.2939785 ↗
  2. Huber, P. J. (1964). Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI: 10.1214/aoms/1177703732 ↗

如何引用本页

ScholarGate. (2026, June 3). Robust XGBoost (Extreme Gradient Boosting with Robust Loss Functions). ScholarGate. https://scholargate.app/zh/machine-learning/robust-xgboost

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ScholarGate — Robust XGBoost (Robust XGBoost (Extreme Gradient Boosting with Robust Loss Functions)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/robust-xgboost · 数据集： https://doi.org/10.5281/zenodo.20539026