## 5 Results and Discussion
### Model Interpretation  
Model interpretation is crucial in predicting rock mass quality classifications. Analyzing feature importance helps to understand the contribution of input parameters to model predictions. This understanding allows for optimizing geological survey and data collection strategies, thereby improving prediction accuracy.  
XGBoost provides various methods for evaluating feature importance, including weight-based, cover-based, and gain-based approaches. The weight-based method calculates the number of times a feature is used to split the data across all decision trees. The more frequently a feature appears at split points, the higher its weight. The cover method measures the number of training data instances that pass through split points using a particular feature, reflecting the feature's prevalence in the model. Gain indicates the average reduction in training loss when a feature is used for splitting, directly reflecting its contribution to model accuracy. Analyzing feature importance through multiple methods offers a comprehensive understanding of each feature's contribution to the model. As shown in  
Figure 9: The ROC curves and AUC values of models: (**a**) XGBoost; (**b**) BO - XGBoost; (**c**) ZOA-XGBoost.  
Figure 10, RQD stands out in all three methods, particularly in gain (0.517), indicating its significant role in improving model performance. UCS and W also show high importance, consistently contributing across all methods, while Kv has relatively lower importance. The feature importance analysis ranks the features as follows: RQD > UCS > W > Kv.  
To determine the impact of features on rock mass quality prediction, individual conditional expectation (ICE) plots were used for detailed analysis of each feature [27], as shown in Figure 11. ICE plots illustrate the effect and direction of a single feature's change on rock mass quality prediction, holding other feature values constant. The pink lines represent the prediction changes of individual instances, while the purple lines show the average prediction changes for all instances. The figure reveals that as RQD increases, particularly in the higher RQD range, the prediction probability for class II rises significantly, while it decreases for class V. At low RQD values, the probability for class III is low, but it increases markedly as RQD grows, indicating that mid to high RQD values favor class III predictions. Similarly, mid to low RQD values support class IV predictions, highlighting RQD's critical impact on rock mass quality prediction, with higher RQD values indicating better rock mass quality. UCS also significantly impacts prediction probability, showing trends similar to RQD for classes III, IV, and V. However, changes in UCS have a smaller effect on class II predictions. The relatively flat lines in the ICE plot for Kv suggest its limited contribution to rock mass classification predictions, whereas W has a slightly higher influence than Kv.  
The ICE plot analysis indicates that RQD and UCS are the most influential features for rock mass quality classification predictions, while W and Kv have relatively minor impacts, consistent with the feature importance analysis results. Future data collection efforts should focus on gathering RQD and UCS to enhance model prediction performance. Although W and Kv have smaller roles in the model, they should not be overlooked, especially under specific geological conditions where they might be significant.  
Figure 10: The relative importance of the total features.