## 3 Results  
In this paper, the effects of different ensemble learning algorithms on the prediction results of the final model were compared with those of a single classifier model. Different filling methods and feature selection algorithms also have influence on the prediction results. To prevent the loss of some important features, we also compared the impact of the data input models with and without feature selection on the results. The entire default risk prediction model process is shown in Figure 4.  
Models such as SVM, DT, KNN, LDA, RF, gcForest, CatBoost, RUSBoost, Imbalance-XGBoost, and Stacking are mainly used in the experiment. The results of the different filling methods, whether feature selection, different feature selection methods, and different models, on user default prediction are compared, respectively. Table 4 shows the impact of the different filling methods on the predicted results. We found that the result of using Mean imputation is better than that of using Hot-deck imputation, especially when using single classifiers. Then, we compared the effects of the different feature selection methods on the model performance, and the results are shown in Table 5. Using the feature selection method based on the regression algorithm, the overall effect of the model is best. In a single classifier, the feature selection using the Elastic Net regres  
Figure 4: Experimental flow chart. DT model have the highest accuracy, which is 98.59%. The second best single classifier model is 97.18%, and its feature selection algorithm is based on the stepwise regression of the BIC criterion. The regression coefficient can well display the relative contribution of features in the model, so features with little or no contribution can be deleted. Table 5 also shows the influence of the different feature selection methods on the results in ensemble learning. The models with better prediction results are RF and CatBoost. The results of the stepwise feature selection based on the AIC criterion were input into the RF model with the best prediction accuracy of 100.00%, and the highest accuracy of CatBoost is 98.59%. Figure 5 shows the average of various indicator values inputted into each model after four regression coefficient feature selection algorithms. The DT indicators values are higher than 95.00%. The RF, CatBoost, RUSBoost, and Stacking indicators have high values and are greater than 93.00%. In particular, the values of CatBoost are greater than 97%. Under Hot-deck imputation, we used the data without feature selection to input each ensemble learning model, and the results are shown in Figure 6, where the index values of CatBoost and RUSBoost reach 100%. We take the optimal results of each ensemble learning model, as shown in Table 6, and the results indicate that Hot-deck imputation is more suitable for ensemble learning model algorithms. Combined with Table 4, the single classifiers are more suitable for Mean imputation. The experimental results of this paper show that the ensemble learning algorithms have better predictive ability for user defaults, and the accuracy of using the CatBoost algorithm to predict user defaults is as high as 100%, with the other indicator values also reaching 100%. The overall effect of ensemble learning is better than that of single classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Hot Deck** & **Mean** \\ \hline SVM & 0.6761 & 0.8451 \\ DT & 0.7746 & 0.9577 \\ KNN & 0.6619 & 0.8732 \\ LDA & 0.7606 & 0.8873 \\ gcForest & 0.9718 & 0.9296 \\ RF & 0.9859 & 0.9859 \\ CatBoost & \(1\) & 0.9859 \\ RUSBoost & \(1\) & 0.9859 \\ Imbalance-XGBoost & 0.9577 & 0.9577 \\ Stacking & 0.9296 & 0.9718 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact of different filling methods on prediction results, and the values in the table represent accuracy.