## 5 Results and Discussion  
### Analysis of Experimental Results  
The comparison between the predicted results and the actual values of the load strength of surrounding rock points from 92 test samples, utilizing the class 3 base learners of the AdaBoost ensemble model, is illustrated in Figure 7.  
To comprehensively evaluate the performance of the model, the determination coefficient (R\({}^{2}\)), mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were calculated, and the results are presented in Table 4. By analyzing the data presented in the table, it is evident that the SVM-based learning model developed in this paper outperforms all other models across various indicators. The R\({}^{2}\) value of the SVM-based learning model on the test set is the highest at 0.972. This represents a significant improvement compared to the R\({}^{2}\) values of the other two models, indicating that the SVM model exhibits a high degree of fit to the data and effectively explaining the variation within it. In contrast, the linear regression-based learning model has the lowest coefficient of determination, suggesting a poor fit. The mean absolute error (MAE) ranges from 1.7 to 9.8 MPa, the root mean square error (RMSE) ranges from 1.9 to 16.5 MPa, and the mean absolute percentage error (MAPE) ranges from 8.1% to 55.7%. Furthermore, the SVM-based ensemble model demonstrates the lowest values in the RMSE, MAE, and MAPE metrics, which are 1.717 MPa, 1.940 MPa, and 8.145%, respectively. This indicates a minimal deviation between the predicted results and the actual values, further substantiating the model's superior predictive accuracy. When the R\({}^{2}\) value of the model approaches 1, it signifies that the model possesses strong explanatory power for the observed data. Additionally, small values of MAE, RMSE, and MAPE indicate that the model demonstrates good predictive ability. This suggests that the model proposed in this article achieves higher prediction accuracy. By employing an SVM-based ensemble learning model to predict the uniaxial compressive strength of the surrounding rock during the coal mine support process, a more effective feedback relationship model can be established. This model characterizes the interaction between the rock and the support system in the tunnel, providing a foundation for adjusting tunnel support parameters. From the analysis presented in the figure, it is evident that various models exhibit strong fitting capabilities for predicting the uniaxial compressive strength of coal and rock masses. Among these, the SVM model demonstrates the most effective predictive performance. The prediction results from this model show minimal errors compared to the true values for the vast majority of test samples, with only a few individual samples exhibiting discrepancies. Following closely is the decision tree model, which also yields  
Figure 7: Prediction results of three types of base learner models. (**a**) Tree-based learner; (**b**) SVM-based learner; (**c**) linear-based learners. prediction results that are relatively near the true values; however, it displays significant deviations in some cases. In contrast, the linear model performs the poorest, showing a substantial difference between the predicted results and the actual values. This indicates that the linear model possesses limited predictive capability for the uniaxial compressive strength of coal and rock masses. To further verify the advantages of the AdaBoost SVM model, we compared it with other advanced models, including the convolutional neural network (CNN) model based on deep learning, the LSTM model, and the random forest (RF) model based on traditional machine learning.