## 3 Results
### Meta-Classifier Performance Evaluation After Late Fusion
The \(p\)-value is higher than the typical 0.05 threshold, indicating no statistically significant difference between the accuracy in the cross-validation and the test set. **T-test for Precision:** The t-stat indicates small differences in precision between the cross-validation and the test set. The \(p\)-value is higher than the typical 0.05 threshold, indicating no statistically significant difference between the precision in the cross-validation and the test set. This also indicates that the precisions in the test set and the cross-validation are consistent. **T-test for Recall:** The t-stat indicates a more noticeable difference in recall between the cross-validation and test set without reaching considerably high values. The \(p\)-value indicates some statistically significant difference between recall in the cross-validation and the test set. This means that the observed difference in recall did not occur by random chance; therefore, there is a meaningful distinction between recall values in the two sets. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **T-Stat** & \(p\)**-Value** \\ \hline
**T-test for Accuracy:** & \(-1.2454\) & 0.2809 \\ \hline
**T-test for Precision:** & \(-0.4129\) & 0.7008 \\ \hline
**T-test for Recall:** & \(-3.5125\) & 0.0246 \\ \hline
**T-test for the F1-Score:** & \(-1.7476\) & 0.1555 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Paired \(t\)-test evaluation metrics for the meta-classifier. Figure 10: Learning curve results of the trained meta-classifier with the late fusion technique. **T-test for the F1-Score:** The t-stat indicates very small differences in the F1-score between the cross-validation and the test set. The \(p\)-value suggests that the F1-score in the test set is consistent with the F1-score observed during the cross-validation. In Figure 12, we can see the performance metrics of the meta-classifier after the final testing. In Figure 13, we can see the meta-classifier's count of predictions per probability range. The results show an improvement in certainty, as the probabilities increase from 0.6 to 1.0, which is a good indication. The lower non-aggression prediction count is acceptable because the aggression class had more entries than the non-aggression class. In summary, in the final meta-classifier evaluation, we observe that the combination of audio and visual data further enhanced the model, providing a more robust application of the multimodal approach for predicting aggressive behaviors in real-world applications. Although the recall for the non-aggression class has some room for improvement, this is an acceptable outcome, considering the class imbalance (606 instances in the aggression class vs. 390 non-aggression-class instances) [50,51]. However, the balanced accuracy indicates that the model is robust and that it handles class imbalances well [52]. The effect of the class imbalance on our model is visible in Figures 9 and 12, and in Figure 13, with the model favoring the majority class (aggression), recall in the non-aggression class is slightly lower (0.84) while being higher in the aggression class (0.98). A balanced accuracy of 87.26% supports the model's bias. Class-balancing mitigation strategies and bias elimination could be applied to the model, such as oversampling techniques, like the synthetic minority oversampling technique (SMOTE) or utilizing the random forest's attribute class_weight = 'balanced', which would increase the minority class's weight. Nonetheless, on the basis of an unbiased comparison among the audio model, the visual model, and the meta-classifier, no class-balancing mitigation strategy was used because in the previous model, none of these class imbalance mitigation techniques were applied. To mitigate this, a cross-validation of 5 \(\times\) 5 folds was used in the visual model from the prior work, in the audio classifier, and in the meta-classifier, providing methodological class imbalance mitigation and eliminating random predictions.