Personal Credit Evaluation Model Based on Bayesian Optimization of LightGBM
Aiming at the problems that traditional credit evaluation models cannot handle large-scale imbalanced data,train-ing time,and inaccurate evaluations,an optimized personal credit evaluation model is proposed.The model is based on the gradient boosting framework LightGBM,combined with the Bayesian global optimization algorithm for personal credit evaluation.In order to verify the applicability of the model,the Lending Club public data set is used to conduct related experiments and compared with the prediction results of logistic regression,random forest,and XGBoost models.The experimental results show that the personal credit evaluation effect of this model is better,the evaluation accuracy rate reaches 99.97%,and the F1-score of minority samples reaches 89.02%.
personal credit evaluationintegrated learningLightGBMhyperparameter optimizationimportance of fea-tures
国家科技期刊平台
NSTL
万方数据
