基于网格搜索和随机森林的汽车信贷违约预测研究

基于某金融机构的汽车信贷违约数据构建随机森林风险预测模型，用主成分分析法对数据进行降维，利用上采样的方法解决样本不平衡的问题，同时通过综合五折交叉验证法和网格搜索对随机森林模型调参。此外，还与其他机器学习算法的预测结果进行比较。研究表明，相对于其他两种预测模型，随机森林的性能都是最优的，性能较佳。同时，采用随机森林计算特征重要性时发现，个人抵押资产的价值对汽车信贷违约有显著的影响。

Research on Automobile Credit Default Prediction Based on Grid Search and Random Forest

Based on the automobile credit default data of a financial institution, a random forest risk prediction model is constructed. The principal component analysis method is used to reduce the dimensions of the data, and the method of up sampling was used to solve the problem of sample imbalance. The random forest model parameters were adjusted by integrating the 50 fold cross validation method and grid search. In addition, the prediction results are compared with those of other machine learning algorithms. The research shows that, compared with the other two prediction models, the performance of random forest is optimal and better. At the same time, when using stochastic forests to calculate the importance of characteristics, the value of personal mortgage assets has a significant impact on automobile credit default.