The Application of Tree-based model to Unbalanced German Credit Data Analysis

摘要：

　　With the development of financial consumption,demand for credit has soared.Since the bank has detailed client data,it is important to build effective models to distinguish between high-risk groups and lowrisk groups.However,traditional credit evaluation methods including expert opinion,credit rating and credit scoring are very subjective and inaccurate.Moreover,the data are highly unbalanced since the number of high-risk groups is significantly less than that of low-risk groups.Progress in machine learning makes it possible to conduct accurate credit analysis.The tree-based machine learning models are particularly suitable for the unbalanced credit data by weighting the credit individuals.We apply a series of tree-based machine learning models to analyze the German Credit Data from the UCI Repository of Machine Learning Databases.

作者: Zhengye Chen

作者单位: Allendale Columbia School,519 Allens Creek Road,Rochester 14618,NY,USA

会议类型: 国际会议

会议名称: 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)(2018第二届电子信息技术与计算机工程国际会议)(EITCE2018)

会议地点: 上海

会议语种:英文

页码: 1-7

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）