## 3 Results
### Performance Comparison (Early vs. Late Fusion)  
The key performance metrics for comparing Early and Late Fusion models are displayed in Table 2. The Late Fusion model outperforms the Early Fusion model in most scores, including recall, accuracy, F1 score, precision, and ROC-AUC, with the most significant improvement being seen in recall. Results indicate that Late Fusion is more effective in identifying cases of aggression, thus suggesting that the integration of audio and visual prediction features enables a robust classification in aggressive incidents, considering the decision level. Considering false positive rates, Early Fusion exhibits higher precision than the Late Fusion mode, which indicates that Early Fusion performs better in minimal false alarm applications, such as real-time monitoring appliances, where an unnecessary intervention could be ineffective and disruptive. On the other hand, Early Fusion's low recall score indicates that it may be ineffective in detecting aggressive behavior instances, which could be problematic in high-risk scenarios where the failure of a timely intervention could cause significant consequences to the patient or caregivers. The plots and metrics for Early and Late Fusion models are presented below in Figures 1 and 2, with corresponding ROC-AUC scores and curves, and a learning curve. Considering ROC curves, Late Fusion shows a better fit on the training data compared tothe Early Fusion ROC curve. A steep slope in the Late Fusion ROC curve highlights its ability to learn well from the training set, and reaches a plateau, whereas Early Fusion's slope is more gradual until 0.7 before reaching a plateau at 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Early Fusion** & **Late Fusion** & **Best Model** \\ \hline Accuracy & 0.8289 & 0.8763 & Late Fusion \\ \hline Precision (PPV) & 0.8526 & 0.8242 & Early Fusion \\ \hline Recall & 0.8182 & 0.9146 & Late Fusion \\ \hline F1-Score & 0.8351 & 0.8671 & Late Fusion \\ \hline ROC-AUC Score & 0.9223 & 0.9708 & Late Fusion \\ \hline Balanced Accuracy & 0.8295 & 0.8804 & Late Fusion \\ \hline Cross-Validated Balanced Accuracy & 0.8337 & 0.8828 & Late Fusion \\ \hline Cross-Validated ROC-AUC Score & 0.9247 & 0.9592 & Late Fusion \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of metrics between Early and Late Fusion models. Figure 1: ROC-AUC curve comparison between Early and Late Fusion meta-classifiers. Figure 2: Learning curve comparison between Early and Late Fusion meta-classifiers. The learning curves of Early and Late Fusion models are displayed in Figure 2. Early Fusion shows an incremental learning from the beginning until 670 training samples, with a minor decline at the end, with a maximum score reached of 0.83. Late Fusion, however, shows a steep inclination until 210 samples and then a plateau with a steady score on the training data, reaching a peak score of 0.89. Even though both models exhibit balanced learning curves, Late Fusion exhibits a more advantageous curve, indicating that the data are fitting well with the model. The plateau in the graph shows that the model learned as well as it could, and further data would not improve the model's ability. Confusion matrix comparison is being displayed in Table 3 and Figure 3, highlighting the model's classification abilities both in positive and negative prediction class cases. Late Fusion reduced the number of false negatives, showing that it is less likely to falsely classify aggressive occurrences. This is advantageous in applications in which failing to detect aggression could have significant consequences. Early Fusion, on the other hand, maintains a slight advantage in false positives, aligning with its improved precision. Final results of the performance metrics were drawn into a multi-axis spider diagram to better visualize strengths and weaknesses in the performance of the two models.