Abstract
Currently, for the problem of personal credit risk identification, the most commonly used method is to optimize the parameters of the model through bionic algorithms to obtain higher accuracy, but it may face the risk of lower precision. Some scholars also discussed the identification of personal credit risk from the perspective of combination models. From the perspective of integrated learning, based on C5.0 algorithm and using boosting technology, this paper constructs the boosting-c5.0 personal credit risk identification model, and uses UCI German personal credit data set to verify the performance of the model. The study found that the accuracy, recall, precision and AUC value of boosting-C5.0 model are better than SVM, logistic and C5.0 models.
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