## 4 Discussion  
In this work, we demonstrate the effects of different adjusted ensemble learning algorithms on the final model prediction compared with a single classifier model and analyze the effects of different filling methods and feature selection algorithms on the prediction results. The results show that an ensemble learning algorithm has better predictive ability in user default prediction, especially when the CatBoost algorithm is used; its prediction accuracy is as high as 100%. Compared with a single classifier, ensemble learning has a better overall effect.  
Ensemble learning algorithms combine predictions from multiple base models to reduce the bias and variance of a single model, thereby improving the overall prediction  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Accuracy** & **Specificity** & **Sensitivity** & **F1-Score** & **Kappa** & **MCC** \\ \hline CatBoost (Hot deck) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ RUSBoost (Hot deck) & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ gcForest (Hot deck) & 97.18\% & 97.29\% & 97.06\% & 97.06\% & 94.36\% & 94.36\% \\ Forest (Mean) & 98.59\% & 97.29\% & 100\% & 98.55\% & 97.18\% & 97.22\% \\ Imbalance (Hot deck) & 95.77\% & 85.71\% & 50\% & 61.54\% & 34.78\% & 37.79\% \\ Stacking (Mean) & 97.18\% & 94.59\% & 100\% & 97.14\% & 94.37\% & 94.52\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of optimal results of all ensemble learning models.  
Figure 5: Average of the values of the indicators for each model using the four regression coefficient feature selection algorithms.  
Figure 6: Results of individual ensemble learning models without feature selection.  
accuracy. Additionally, due to the utilization of multiple diverse base models, ensemble learning models exhibit better robustness, maintaining good predictive performance across different data distributions or feature conditions. In our research, we found that traditional single machine learning models suffer from poor interpretability, making it difficult to capture nonlinear relationships and exhibiting shortcomings in both prediction accuracy and robustness. The heterogeneous and homogeneous ensemble learning model for user default prediction efficiently handles large volumes of high-dimensional data and enhances the model generalization capabilities. By effectively leveraging predictions from multiple models, it mitigates the risk of overfitting, thereby improving the model's generalization ability.  
In addition, the results from using Mean imputation were found to be better than those from using Hot-deck imputation, especially when using single classifiers. The reason for this difference is that Hot-deck imputation requires finding all the samples without null values, but in practice, it is difficult to find a perfect Hot-deck imputation dataset. Therefore, the overall accuracy of the model using Mean imputation is better than that of Hot-deck imputation.  
Future research directions could include the further optimization of ensemble learning algorithms, the exploration of more efficient feature selection methods, and deeper analysis and processing methods for unbalanced data.