关键词: XGBoost, 特征选择, 机器学习, 信贷评估

Abstract:

Since the installment payment method of e-commerce platform and P2P credit platform is keeping promoted. how to mine the potential user model and how to evaluate the credit risk of unknown users from the huge amount of user credit data, so as to reduce the risk of credit business, has become the mainstream of research. Aiming at the problem that the existing methods can not deal with high-dimensional credit data efficiently, this paper adopts a series of data preprocessing methods and feature selection method XGBFS（XGBoost Feature Selection） which is based on embedded idea. This method can reduce the dimension of user credit data, work out the XGBoost evaluation model and finally realize the user credit risk assessment. Compared with the existing methods, experimental results show that this method can select important features from high-dimensional data. Besides, the classifier has a more outstanding performance in precision and recall rate.

Key words: XGBoost, feature selection, machine learning, credit evaluation