## 4 Results
### Model Performance Comparison  
This paper compares the performance of various machine-learning models on different datasets. Table 3 presents the goodness-of-fit and complexity of each model, while Table 4 reports the predictive capabilities of the models.  
According to Figure 6, the random forest shows the best goodness-of-fit (explanatory power) across all datasets. In the Image dataset, the R\({}^{2}\) value of the random forest model is 0.8557, which is significantly higher than that of other models, indicating its strong performance in capturing the relationship between image features and stress prediction. Similarly, in the Integrated dataset, the R\({}^{2}\) value of the random forest model increases to 0.8745, demonstrating an exceptional fitting ability. In contrast, the KNN model consistently performs poorly across all datasets, with an R\({}^{2}\) value of only 0.3424 in the Image dataset, highlighting its significant shortcomings in handling complex data. At the same time, the AIC and BIC values for the random forest and AdaBoost models are lower across all datasets, indicating good model simplicity. This suggests that the model provides a high explanatory power with a relatively low complexity, making it more concise and efficient.  
Based on the results in Figure 6, it is evident that the random forest and AdaBoost models exhibit strong predictive capabilities. In the Integrated dataset, the random forest model achieves an RMSE of 0.0688, MAE of 0.0412, SI of 0.7267, and MBE of \(-0.0026\), all representing the best performance, indicating strong capabilities in controlling prediction errors. In contrast, the KNN model has poorer predictive capabilities, with an RMSE of 0.1452 and SI of 1.5335 in the Basic dataset, significantly higher than other models, indicating larger prediction errors and a lower model accuracy and stability. The KGE indicator comparisons also support these conclusions. The KGE for the random forest model reaches 0.4033 in the Basic dataset, and 0.8304 in the Image dataset, and further increases to 0.8620 in the Integrated dataset, outperforming other models and highlighting its superiority in stress prediction tasks. In contrast, the KNN model has the lowest KGE across all three datasets, with a worse performance in the Image dataset compared to the Basic dataset (whereas other models show varying degrees of improvement), indicating that the KNN model is not suitable for this type of task.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MBE** & **SI** \\ \hline Lasso & 0.0880 & 0.0601 & \(-0.0083\) & 0.9290 \\ KNN & 0.1388 & 0.1024 & \(-0.0188\) & 1.4655 \\ XGBoost & 0.0908 & 0.0574 & \(-0.0091\) & 0.9582 \\ RandomForest & 0.0688 & 0.0412 & \(-0.0026\) & 0.7267 \\ AdaBoost & 0.0690 & 0.0465 & \(-0.0044\) & 0.7284 \\ ANN & 0.1017 & 0.0767 & \(-0.0043\) & 1.0738 \\ ElasticNet & 0.0882 & 0.0602 & \(-0.0087\) & 0.9310 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Model Predictive Capabilities.  
Figure 6: Visualization of model performance comparison. Each subplot illustrates the model’s predictive performance during the testing phase. The dashed line in each subplot represents the scenario where predicted values are equal to the true values, indicating a perfect fit. The solid line is the linear regression line of the predicted values against the observed true values. The closer the solid line is to the dashed line, the more accurate the model’s predictions are, indicating stronger performance.