Abstract
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.
Highlights
As an unsecured credit facility, credit cards have huge risks behind the high returns of banks
The results show that the area under the curve (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
Summary
As an unsecured credit facility, credit cards have huge risks behind the high returns of banks. Credit card default prediction is based on the historical data of credit card customers. The use of corresponding methods to predict and analyze credit card customer default behavior is a typical classification problem. Thomas [1] used discriminant analysis to score the credits and behaviors of borrowers; Yeh and Lien [2] used Logistic regression, decision trees, artificial neural networks and other algorithms to predict customer default payments in Taiwan, and compared the predictions of these algorithms. The results show that the accuracy of random forest prediction is higher than that of Lasso-Logistic
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