A Study on RB‐XGBoost Algorithm‐Based e‐Commerce Credit Risk Assessment Model

Abstract

The current method’s e-commerce credit risk assessment is prone to poor data balance and low evaluation accuracy. An RB-XGBoost algorithm-based e-commerce credit risk assessment model is proposed in this study. The adaptive random balance (RB) method is used to sample and process the obtained data to improve the balance degree of the data. An assessment index system is constructed based on the processed data. Based on the risk evaluation index system and the XGBoost algorithm, this paper constructed an e-commerce risk assessment model and assessed the e-commerce credit risk using this model. The experimental results show that the proposed method has good data balance, a high kappa coefficient, and a large receiver operating characteristic (ROC) curve area, which can effectively improve e-commerce credit risk assessment accuracy.

Classification and identification of foodborne pathogenic bacteria by Raman spectroscopy based on PCA and LightGBM algorithm

“…Leaf growth strategy was adopted by leaf-wise with depth restriction. As an optimization of GBDT model, LightGBM inherits the integrated algorithm capability of XGBoost [10] [11] and adopts efficient parallel training design, so it has better accuracy of classification, quicker training speed and lower memory consumption.…”