XGBoost Model and Its Application to Personal Credit Evaluation

Abstract

This article investigates the application of the eXtreme Gradient Boosting (XGB) method to the credit evaluation problem based on big data. We first study the theoretical modeling of the credit classification problem using XGB algorithm, and then we apply the XGB model to the personal loan scenario based on the open data set from Lending Club Platform in USA. The empirical study shows that the XGB model has obvious advantages in both feature selection and classification performance compared to the logistic regression and the other three tree-based models.