Comparison of Several Data Mining Methods in Credit Card Default Prediction

• 作者: Haomin Zhang Shenghui Yang
• 来源期刊: 智能信息管理(英文)
• 学科: 医学
• 关键词: LightGBM Xgboost AUC F1-Score Data MINING
• 年，卷（期）: 2018,（5）
• 页码范围: 115-122
• 页数: 8页
• 分类号: R73

摘要

LightGBM is an open-source, distributed and high-performance GB framework built by Microsoft company. LightGBM has some advantages such as fast learning speed, high parallelism efficiency and high-volume data, and so on. Based on the open data set of credit card in Taiwan, five data mining methods, Logistic regression, SVM, neural network, Xgboost and LightGBM, are compared in this paper. The results show that the AUC, F1-Score and the predictive correct ratio of LightGBM are the best, and that of Xgboost is second. It indicates that LightGBM or Xgboost has a good performance in the prediction of categorical response variables and has a good application value in the big data era.