## 3 Results
### Trained Model's Performance
#### 3.1.1 Classification Performance in the Training Set Using Random Forest  
Our model shows excellent performance in the test set, with high scores in all the metrics. The high level of performance indicates that the specific dataset that was used during the training aids the classifier to improve while providing a clear distinction between the classes. Although the high performance of the model during the training might indicate potential overfitting, the choice to use a very low percentage of the holistic dataset (10%) for training is proof that the model does not overfit and generalizes well in the training set. Cross-validation was also used to mitigate this and prevent the model from learning patterns in the specific set of data. This ensures good model generalization and prevents overfitting. Just like in the prior visual model, the models display the ability to generalize well for the data because of the low standard deviations across the folds combined with the high performance metrics Figure 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Visual\_Filename** & **Audio\_Filename** & **True\_Label\_Visual** & **True\_Label\_Audio** & **Refined\_True\_Label** & **Predicted\_Label** & **Probabilities** \\ \hline image\_00721\_jpg & filename\_0 & Aggressive & aggressive & aggressive & 1 & [0.13, 0.93] \\ \hline image\_00467\_jpg & filename\_1 & Aggressive & aggressive & aggressive & 1 & [0.26, 069] \\ \hline image\_00556\_jpg & filename\_2 & Aggressive & non-aggressive & non-aggressive & 0 & [0.86, 0.17] \\ \hline image\_00680\_jpg & filename\_3 & Aggressive & aggressive & aggressive & 1 & [0.15, 0.82] \\ \hline... & &... & &... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample from merged_results.csv file. \begin{table}
\begin{tabular}{c c} \hline \hline
**Cross-Validation Scores (Five Folds)** & [0.92875, 0.81233766, 0.87595908, 0.93849206, 0.86296296] \\ \hline
**Mean Cross-Validation Score:** & 0.8837003536153153 \\ \hline
**Inference Time:** & 0.0955 s \\ \hline
**Balanced Accuracy Before Cross-validation:** & 0.92875 \\ \hline ROC-AUC Score: & 0.98875 \\ \hline Precision: & 0.9375 \\ \hline
**Recall:** & 0.9375 \\ \hline
**F1-Score:** & 0.9375 \\ \hline
**Accuracy:** & 0.9298 \\ \hline
**Positive Predictive Values:** & 0.9375 \\ \hline
**Negative Predictive Values:** & 0.92 \\ \hline
**Balanced Accuracy After Cross Validation:** & 0.92875 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model’s evaluation metrics after the training. **Cross-validation scores (five folds):** This represents the model accuracy for the specified folds (five folds) of the data. The scores, with a high value of 0.93 and a low value of 0.81, indicate that the model might perform consistently across some splits of the data, but this is acceptable, considering the imbalances of the data. **Mean cross-validation score:** This represents the average accuracy for all five folds during the cross-validation and gives a more robust estimation of the performance of the model for unseen data. The score of 0.88 indicates solid generalizability, showcasing a robust model. **Inference Time:** This is a crucial metric for applications in real time, which represents the time the model needs to make predictions based on new data. The score of 0.09 proves the suitability of the model for real-time applications. **Balanced Accuracy Before Cross-validation:** This helps in the case of imbalanced classes depicting the average of the recall gathered in each class. The score of 0.92 provides an excellent result in a dataset with class imbalances, like ours. **ROC-AUC Score:** This measures the model's ability to distinguish between positive and negative classes, with a score of 1 being a perfect performance. The score of 0.9 highlights a strong model capable of distinguishing between classes. **Precision:** This is the percentage of actual positive instances out of all the instances the model evaluated as positive. The score of 0.93 means that the model predicts correct positive classes 93.75% of the time.