基于混合特征选择模型CatBoost-LightGBM的违约风险预测研究

Default Risk Prediction Research Based on Hybrid Feature Selection Model Catboost-LightgBM

疫情后,互联网消费金融在国民经济复苏增长中发挥积极作用,但因其产品本身特殊性及过快的发展性,也伴随大量的风险。文中在算法可解析性、模型应用性(识别性、准确性、低成本、稳定性)基础上构建了混合特征选择模型CatBoost-LightGBM,并将此模型应用于某知名信贷平台。结果表明,混合特征选择模型CatBoost-LightGBM在综合评价上显著优于单一模型,对基础模型LR有0.19的提升,对基础特征的LightGBM、XGboost等模型有0.03的提升。

After the epidemic,internet consumer finance plays a positive role in the recovery and growth of the national economy,but due to the particularity and rapid development of its products,it is also accompanied by a large number of risks.In this paper,a hybrid feature selection model catboost-LightgBM is constructed on the basis of the analytical ability of the algorithm and the application of the model.Finally,the model is applied to a well-known credit platform.The results show that the hybrid feature selection model catboost-LightgBM is significantly better than the single model in the comprehensive evaluation.It improves the basic model LR by 0.19 and the lightgbm,xgboost and other models with basic features by 0.03.