## 4 Comparison Analysis of Prediction Models  
### Prediction Accuracy Comparison  
To evaluate the predictive performance of the three models--BP neural network, GA-BP neural network, and XGBoost--a comparative analysis was conducted using the Heatmap of the Confusion Matrix, Receiver Operating Characteristic (ROC) Curves, and key evaluation metrics. The analysis focuses on the models' ability to distinguish between avoidant and non-avoidant behaviors while ensuring stability and reliability. Additionally, we assessed each model's training time and computational efficiency, which are important considerations for real-world implementation. The BP neural network required approximately 6 s for training, while the GA-BP model took around 7 s. In contrast, XGBoost demonstrated faster training, with a time of approximately 3 s. Although BP and GA-BP models required more computational resources, they provided more accurate predictions. Among the three models, GA-BP exhibited the highest efficiency. The confusion matrices provide insights into the classification performance on both training and test datasets. The training dataset results for the BP neural network model indicated successful predictions of 38 non-avoidances and 44 avoidances, with 28 incorrect classifications. On the test dataset, 22 non-avoidances and 27 avoidances were correctly classified, while 18 instances were misclassified. The GA-BP neural network improved predictive accuracy, with 37 non-avoidances and 45 avoidances correctly identified in the training dataset, and reduced misclassifications to 18. On the test dataset, this model further improved, with 25 non-avoidances and 28 avoidances correctly predicted, while the number of incorrect classifications was reduced to 14. The Heatmap of the Confusion Matrix for BP and GA-BP is shown in Figure 6, while that for XGBoost is presented in Figure 7. The XGBoost model, in contrast, achieved the highest accuracy in the training set, correctly classifying 46 non-avoidances and 50 avoidances, with only four misclassifications. However, its performance on the test set declined, with 21 correct non-avoidance classifications and 27 correct avoidance classifications, accompanied by 19 misclassifications. Further insights into model performance were obtained through ROC curves, with AUC values reflecting the models' ability to distinguish between the two classes. The GA-BP neural network exhibited the highest AUC score of 0.84, followed by the BP neural network at 0.80 and XGBoost at 0.74. These results suggest that the GA-BP model offers better discrimination ability in classification tasks than the other two models. The ROC curves for BP, GA-BP, and XGBoost are shown in Figure 8.  
Figure 6: Heatmap of the Confusion Matrix for BP and GA-BP Models. of 0.731 and an F1-score of 0.75, while XGBoost recorded the lowest accuracy at 0.720 and an F1-score of 0.74. Additionally, the algorithmic error for XGBoost was significantly lower than the other two models (0.21), whereas BP and GA-BP neural networks exhibited algorithmic errors of 10.753 and 11.038, respectively. The lower algorithmic error in XGBoost suggests that the model optimizes its loss function more effectively, leading to better convergence during training. This discrepancy in algorithmic error values is primarily due to the difference in loss functions used by the models. BP and GA-BP neural networks typically employ Cross-Entropy Loss, which results in larger numerical values due to their cumulative nature. In contrast, XGBoost utilizes a gradient boosting framework with loss functions (Taylor expansion) that inherently normalize error values, leading to a significantly lower reported error. Despite this lower error, XGBoost did not achieve the highest classification performance, indicating that while it minimized its loss effectively, it did not generalize as well as GA-BP in distinguishing between the two classes.