## 3 Results
### Meta-Classifier Performance Evaluation After Late Fusion  
After applying the late fusion rule to the **visual_detection_results.csv** and **audio_detection_results.csv** files, we loaded the **dataset merged_detection_results.csv** file, consisting of the merged results, and performed an identical pipeline with those of the two previous models to develop a meta-classifier. The meta-classifier reads the data, imputes missing values (if any), standardizes the features, and, using 5k-fold cross-validation, trains the random forest classifier using a testing-training split of 80-20 to make predictions for the combined detection results. The final results are shown in Tables 8-10. **Accuracy: 88.0%--**-A high value, indicating the ability of the model to predict correctly. **Precision: 87.60%--**-The prediction of the positive class (aggressive) by the model indicates that it is correct 87% of the time. **Recall: 92.17%--**-An indication that the model is identifying correct true positives and that the missed predictions are few. **F1-Score: 89.83%--**-The harmonic mean of the model, between the precision and recall, is high, showcasing a good balance. **Balanced Accuracy: 87.26%--**-An indication that the model performs well across both classes and not only in the dominant class. **Inference Time--**-Overall, the inference time is 0.0030 s, which means that the model ensures quick predictions in real-world scenario applications. **Positive Predictive Value (PPV): 87.60%--**-The prediction of the positive class (aggressive) by the model indicates that it is correct 87% of the time. **Negative Predictive Value (NPV): 88.61%--**-This indicates a high prediction accuracy in the non-aggressive class. **Cross-Validated Balanced Accuracy: 86.82%--**-This score is similar to the balanced accuracy of the test set, proving the reliability of the model. **Cross-validated ROC-AUC score: 95.52%--**-This depicts the good generalization ability and robustness of the model. Confusion Matrix Figure 9  
Figure 8: ROC-AUC score of the trained meta-classifier with the late fusion technique. **True Positives (TPs)**: 106--Instances of the positive class correctly identified by the model. **True Negatives (TNs)**: 70--Instances of the negative class correctly identified by the model. **False Positives (FPs)**: 15--Instances of the negative class incorrectly identified by the model. **False Negatives (FNs)**: 9--Instances of the positive class incorrectly identified by the model. **Accuracy for Aggression Detections:** 92%--From all the true labels as aggressive, the model identified 92% of them as actually aggressive. **Accuracy for Argument Detections:** 82%--From all the true labels as non-aggressive, the model identified 82% of them as actually non-aggressive. **Learning Curve:** The training score showcases that the model performs extremely well with the training data, but the performance slightly drops from around 460 to 550 samples and then increases steadily while making a plateau. This behavior is expected because the data have never been seen by the model and were not balanced, with the non-aggression class having fewer entries than the aggression class; therefore, this drop is insignificant. This is also the reason the training score remains stable at 1.0 (which is desirable), but the cross-validation score is trying to reach it Figure 10.  
Figure 9: Confusion matrix results of the trained meta-classifier with the late fusion technique. Paired \(t\)-test Analysis  
We performed a \(t\)-test analysis on the meta-classifier to evaluate if there is a statistically significant difference between the evaluation metrics and the test set. In all our metrics, the \(p\)-value is greater than 0.05, eliminating the indication of statistically significant differences in the performance of the model between the cross-validation and the test set. Below, in Table 11 and Figure 11, the results are presented. **T-test for Accuracy:** The t-stat indicates small differences in accuracy between the cross-validation and the test set.