## 4 Results
### Knowledge Mining Based on SHAP  
Random forest, which exhibits the best performance, is often regarded as a \"black box\" model, meaning its internal decision-making process and the role of features are difficult to understand [52]. Unfortunately, most stress prediction studies using machine-learning models focus on enhancing model performance without addressing the model's interpretability. Therefore, this study attempts to introduce SHapley Additive exPlanations (SHAP) into the field of stress prediction research, using this tool to interpret the best-performing RF. The core idea of SHAP originates from the Shapley values in cooperative game theory, and is particularly well-suited for applications that require high transparency and interpretability [53; 54]. Figure 7 presents a bar chart showing the importance ranking of all features in the model. It is evident that the most important feature is \"front_PCA1\", which is the first principal component obtained from the front view using the pre-trained model and PCA algorithm. This is followed by Roof Subsidence, a traditional geological parameter. Features ranked 3 to 5 include Surface Elevation, Burial Depth, and side_tSNE1. A closer examination of the top ten features reveals that six of them are from image data (front_PCA1, side_tSNE1, side_PCA5, front_PCA5, side_PCA2, and front_tSNE3). This indicates that image features are even more important in the model compared to traditional geological parameters, making them a key factor in predicting roof stress. This further confirms the effectiveness and significance of image data in stress prediction. Figure 6 is a heatmap of feature correlations, which further supports the findings of Figure 8. It shows a high positive correlation (0.6906) between front_PCA1 and Roof Subsidence. This suggests that these features may reflect certain common geological phenomena or spatial patterns--areas with roof subsidence often show higher stress concentrations as reflected by the front_PCA1 image feature. Combining the existing domain knowledge, we believe that these features may reflect certain common geological phenomena or spatial patterns: areas with roof settlement often show higher stress concentration, as indicated by the front_PCA1 image feature. This finding also demonstrates that image features are not merely a black box but can be understood and used for knowledge extraction, provided that the appropriate tools are employed, such as SHAP. Figure 9 shows a beeswarm plot, which illustrates how the values of each feature affect the model output. Each point in the plot represents a sample, with colors ranging from blue to red indicating the range of feature values from low to high. The distribution of SHAP values along the \(X\)-axis represents the positive or negative impact of each feature on the model output (i.e., roof stress). It is apparent from the plot that front_PCA1 is the most crucial feature, with a wide range of SHAP values indicating its substantial impact on stress prediction. When front_PCA1 has higher values (red points), its SHAP values are also higher, indicating that a high value of this feature significantly increases the predicted roof stress. Similarly, Roof Subsidence shows a positive impact on stress prediction, with higher SHAP values corresponding to greater stress predictions, which aligns with the engineering observation that increased roof subsidence typically leads to higher stress concentration. Surface Elevation, though ranked high in importance, has a relatively small range of SHAP values, indicating a more stable effect on stress prediction. Changes in elevation do not significantly alter stress predictions compared to the first two features. From this analysis, three obvious points of domain knowledge can be drawn. First, the predictive role of image features does not operate in isolation; rather, there are interactive effects with other features, such as geological parameters.