Research on Credit Risk Prediction Models Based on Machine Learning

This study evaluates the performance of various machine learning algorithms in credit risk prediction and compares the performance of models such as logistic regression, decision tree, random forest and support vector machine in detail. The indicators include accuracy, precision, recall and F1 value. The results show that the random forest model performs best in all indicators, with an accuracy of 88.3%, precision and recall of 87.5% and 86.8% respectively, showing its superiority in processing complex data patterns and high-dimensional features. The support vector machine also performs well in high-dimensional data processing, with an accuracy of 84.5%, but the training time is long and there is a computational bottleneck when processing large-scale data. In contrast, the logistic regression model performs poorly, with an accuracy of 78.4%. Although it is effective for linear data, it is limited when dealing with complex nonlinear data; the decision tree model has an accuracy of 81.6%, which has good interpretability and ease of use, but is prone to overfitting when processing high-dimensional data. The study also found that data preprocessing and feature selection are crucial to improving the performance of the model, emphasizing the importance of data cleaning and optimization. Random forest has obvious advantages in credit risk prediction.