Research on Credit Risk Evaluation Model Based on RF-FL-LightGBM Algorithm
In order to solve the problem of high-dimensional sparse customer credit characteristics and sample imbalance in the big data environment,thereby improving the accuracy of customer credit evaluation,this paper proposes a credit risk evaluation model based on the RF-FL-LightGBM algorithm.First,random forest(RF)is used to sort and filter the importance of high-dimen-sional features to eliminate features that easily lead to model overfitting and redundant uselessness.Secondly,the two-category bal-anced cross-straight loss function(FL)is improved based on the Focal Loss function.As the loss function of the LightGBM model to improve the model accuracy due to the positive and negative samples imbalance,thereby improving the model classification perfor-mance.Experiments use the historical customer data set of a financial leasing company.The results show that the F1-Score and AUC of the RF-FL-LightGBM model are significantly higher than the XGBoost and LigthGBM models.The RF-FL-LightGBM algo-rithm not only effectively processes high-dimensional sparse and unbalanced sample data,but also improves the customer attributes classification accuracy and has higher execution efficiency.
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