Personal credit risk identification based on combined machine learning model

Using a single model to evaluate personal credit risk may face problems such as low total prediction accuracy of a single model, poor interpretability, and high type II error rate. Based on this, from the perspective of combined model, this article combines statistical modeling with non-statistical modeling to construct a C5.0-SVM combined model.