基于组合特征选择的随机森林信用评估

Random Forest Credit Evaluation Based on Combination Feature Selection

构建个人信用风险评估模型的过程中,特征工程很大程度上决定了评估器的性能,传统的特征选择方法无法全面的考虑高维度指标对评估结果的影响,且大多数研究在构建模型的过程中人为决定特征集大小,导致随机性强、可信度低;基于此,提出基于传统风控指标优化XGBoost的随机森林模型(IV-XGBoostRF),将传统风控指标IV与XGBoost相结合对原始特征集进行筛选,建立较为完善的信用评估模型.通过对比实验的结果显示改进后的随机森林模型准确度提高了0.90%,且其他各项评估指标均优于传统信用评估模型,证明了该组合特征选择方法的可行性,有一定的应用价值.

In the process of building a personal credit risk evaluation model,feature engineering largely determines the performance of the evaluator.Traditional feature selection methods cannot fully consider the impact of high-dimensional indicators on the evaluation results,and most studies artificially determines the size of the feature set in the process of building the model,leading to high randomness and low credibility.Therefore,a random forest model(IV-XGBoostRF)based on traditional risk control indicators to optimize XGBoost is proposed.The traditional risk control indicators IV and XGBoost are combined to screen the original feature set to build a relatively complete credit evaluation model.The results of comparison experiments show that the accuracy of the improved random forest model is increased by 0.90%,and other evaluation indicators are better than the traditional credit evaluation model,which proves the feasibility of the feature selection method and has certain application value.