统计与决策2026，Vol.42Issue(5)：47-53,7.DOI:10.13546/j.cnki.tjyjc.2026.05.008
基于Huber损失函数的稳健随机森林模型及应用
Robust Random Forest Model Based on Huber Loss Function and Its Application
摘要
Abstract
With the development of artificial intelligence technology,random forest model has been rapidly developed and widely used in many fields.However,in practical problems,the traditional random forest model is vulnerable to heavy-tailed data-sets or outliers,resulting in estimation bias.In view of this,this paper proposes a robust random forest model based on Huber loss function(HuberRF),and gives the estimation algorithm,variable importance measurement method,and partial dependence rela-tionship measurement method.With the advantage of Huber loss function,this model has better robustness when dealing with data with skewed distribution or outliers,and can better weaken the adverse impact of extreme outliers on model estimation.The nu-merical simulation results show that when dealing with data with skewed distribution or outliers,the robust random forest model based on Huber loss function significantly outperforms the mean regression forest model and the median regression forest model in terms of prediction performance.When the robust random forest model based on Huber loss function is applied to the data set of China's county digital finance and farmers'income,the results indicate that the proposed method has better robustness and great-er prediction ability than the traditional random forest models.关键词
随机森林模型/Huber损失函数/预测误差/稳健性Key words
random forest model/Huber loss function/prediction error/robustness分类
数理科学引用本文复制引用
蔡超,胡成翔..基于Huber损失函数的稳健随机森林模型及应用[J].统计与决策,2026,42(5):47-53,7.基金项目
国家社会科学基金资助项目(24BTJ055) （24BTJ055）
山东省社会科学规划项目(24CTJJ03) （24CTJJ03）
