## 4 Results and Discussion
### Model Construction  
The following two figures display the simulation results of the ISSA-RF model using the collected student data for graduate admissions evaluation. Figure 3 presents the comparison between the predicted outcomes and the actual outcomes during the model training period. The green bars in the figure vividly display the discrepancies between the predicted values and the actual values. Generally speaking, the model shows high prediction accuracy throughout the training phase. Although there are some instances where the predicted values deviate significantly from the actual values, the majority of these deviations are below 0.05, indicating that the model effectively captures the relationship between student data and evaluation outcomes [63; 64]. Figure 2: Hyperparameter tuning. Figure 4 reveals a more pronounced trend during the testing phase. There are some larger differences, which may be attributed to noise or outliers in the collected data. However, these anomalies have a limited impact on the overall predictive performance of the model. Despite a few larger discrepancies, the model maintains high prediction accuracy, further validating its robustness in practical applications. This robustness is not only reflected in the model's fit to the training data but also in its ability to provide reliable predictions when dealing with unseen data. This capability is particularly crucial for graduate admissions evaluation, as it involves predicting students' future academic potential and research abilities. It helps the admissions committee make more scientific and rational decisions, providing students with a fairer competitive opportunity. Figure 4: Test set for the actual and predicted admit. Figure 3: Training set for the actual and predicted admission. The provided graph illustrates only the discrepancy between experimental and actual values. There is a lack of intuitive data by which to express the quality of the model training results. Therefore, the data points should be presented in the form of Figure 5a. The model training results are judged by the specific values of root mean Square error (RMSE) and the correlation coefficient (R), thereby reflecting the predictive accuracy of the model. In the model training phase, as depicted in Figure 5a, the discrepancy between the predicted results and actual values is kept within a narrow range. Most predicted points  
Figure 5: A comparison of the admit for actual and predicted data: (**a**) training dataset; (**b**) testing dataset. are closely related to the central line, with only a few data points slightly deviating from the expected trajectory. This close fit is consistent with the statistical data results, with an RMSE of 0.0543 and a correlation coefficient (R) as high as 0.9281. This indicates that the model has a high predictive accuracy for the training data, accurately capturing the complex relationship between input features and output results. However, it is important to be cautious about potential overfitting phenomena. The results may be due to overfitting between the data and the model during the training phase, leading to a very high similarity between predicted and actual data. This could harm the practical application of the model. Figure 5b shows the results of testing with the remaining 30% of the data. The difference between the predicted and actual values has increased, but the overall performance is still satisfactory. The RMSE during the testing phase is 0.0653, and the correlation coefficient (R) is 0.8803. Although the predictive accuracy during the testing phase is slightly lower than during the training phase, this difference is within an acceptable range. This implies that the model works well with the training data and holds high accuracy in prediction and generalization power when dealing with unseen test data. This robustness further validates the effectiveness of the model, indicating that the high precision of the results obtained during the training phase is not due to overfitting phenomena.