用于回归的鲁棒XGBoosting
Robust XGBoosting for Regression
- KU Leuven（鲁汶大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对XGBoost易受垂直异常值和杠杆点影响的问题，提出基于鲁棒回归估计量的MM-XGBoost两步方法，实现了鲁棒性与预测精度的最优权衡。
AI中文摘要：
XGBoost是一种非常流行且强大的预测方法，它迭代地将简单决策树拟合到前一步的残差上，具备高效且可扩展的实现。XGBoost的标准损失函数为二次损失，但也可使用Huber损失。本文研究了XGBoost的鲁棒性，表明其性能会受垂直异常值和杠杆点影响。为解决该问题，探索了基于鲁棒回归中M-、S-和τ-估计量的替代损失函数，结果显示名为MM-XGBoost的两步方法在鲁棒性与预测精度间实现了最佳权衡。
英文摘要：
XGBoost is a very popular and powerful method for prediction. It iteratively fits simple decision trees to the residuals of the previous step. An efficient and scalable implementation is available. The standard loss function for XGBoost is the quadratic loss, but a Huber loss can also be used. In this paper, we study the robustness of XGBoost and show that its performance can be affected by vertical outliers and leverage points. To address this, we explore alternative loss functions, based on M-, S-, and τ -estimators from robust regression. Our results indicate that a two-step procedure, referred to as MM-XGBoost, provides the best trade-off between robustness and prediction accuracy.
