## 4 Results and Discussion
### Ten-Fold Cross-Validation Result  
Although preliminary experimental results show that the composite model has good predictive performance in graduate admissions evaluation, these results are based on specific data sets. In practical application, the model will be faced with a variety of new data, requiring the model to have strong generalizability if it is to effectively deal with these unknown data. Therefore, performing 10-fold cross-validation is a key step to further evaluate the model's performance and generalization ability on a specific dataset. The 10-fold cross-validation method divides the data set into ten parts, one part is selected as the test set at a time, and the remaining nine parts are used for training the model. In this way, every data point has the opportunity to be used as a test, thus ensuring the robustness of the model over different data distributions and avoiding special results that may occur during data acquisition. After 10 iterations, the mean and standard deviation of the model's performance can be calculated, which helps to obtain a more accurate understanding of the model's predictive power and generalization potential. In addition, the 10-fold cross-validation results can be used to determine if the model shows signs of overfitting or underfitting. From the above experimental results, it can be observed that the model's results during the training stage are highly similar to the actual data, which might lead to overfitting. This phenomenon can be further judged and analyzed by the results of 10-fold cross-validation. The performance of the model fluctuates greatly over different data folds, which may indicate that the model is too sensitive to the training data, i.e., that the model has overfitting conditions and lacks the necessary generalization ability. Through the careful analysis of the cross-validation results, the stability and reliability of the model can be evaluated, and the necessary adjustment and optimization of the model can be carried out through the evaluation results in order to improve the effectiveness of the model in practical application. Based on the test results provided (Figure 6), it can be observed that the model fits the data set to a high degree, the results are similar in each test, and that there is no excessive fluctuation. Specifically, even under the most unfavorable conditions, the RMS error is only 0.071. This value is significantly lower than the generally acceptable threshold, indicating that the model not only captures the underlying pattern of the data set but also has excellent generalizability. At the same time, it is proved that the above experimental data are reasonable, and that the model does not overfit in the training stage of the data set. This ability to generalize means that the model can maintain high prediction accuracy even in the face of unknown data. In addition, low RMSE values indicate little deviation between model predictions and actual observed values, which is particularly important in practical applications. When building predictive models for graduate school admissions, for example, this high level of predictive accuracy will help admissions committees more accurately assess applicants' potential, leading to more informed and well-reasoned admissions decisions. The results from the testing experiments indicate that the ISSA-RF model used in this study achieves high predictive accuracy and provides effective data support for graduate admissions prediction. However, the construction of the model is not straightforward and involves several key feature parameters (CGPA, GRE score, SOP, LOR, university rating, research, TOEFL score). From the data alone, it is not possible to determine the impact of each feature on the admissions prediction results. While all of these features play important roles in the model, their contributions to the final prediction results vary.