aiqbench analysis
This research introduces MM-XGBoost, a robust variant of the popular gradient boosting algorithm designed to maintain prediction accuracy in the presence of vertical outliers and leverage points. By integrating M-, S-, and tau-estimators into a two-step procedure, the study offers a practical solution for enhancing model resilience without sacrificing scalability. This advancement is significant for machine learning practitioners seeking more reliable predictive models in noisy data environments.
Robust XGBoosting for Regression
arXiv:2608.13590v1 Announce Type: new 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 explore alternative loss functions, based on M-, S-, and {\tau} -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.
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 explore alternative loss functions, based on M-, S-, and {\tau} -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.
Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML)
arXiv:2608.13590 [cs.LG]
(or arXiv:2608.13590v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.13590 Focus to learn more arXiv-issued DOI via DataCite
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