鲁棒XGBoost回归：基于稳健损失函数的改进

AI 摘要

本文研究了XGBoost回归对异常值的鲁棒性，发现垂直异常值和杠杆点会影响其性能。作者提出了基于M、S和MM估计器的替代损失函数，并引入MM-XGBoost两步法，在鲁棒性和预测精度之间取得了最佳平衡。模拟研究表明该方法有效。

正文节选

Robust XGBoosting for Regression Abstract 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 explo

发布时间：2026-08-17 12:00

抓取时间：2026-08-17 12:15

来源机构：arXiv