## 3 Results
### Final Model Performance  
Following the training and model evaluation of the Random Forest Classifier, the pictures that remained unused during training were now used during testing in order to avoid misleading performance metrics, poor generalization and unreliable testing evaluation. By presenting new, unseen data in our model, we ensure its neutrality. The final results were derived after two separate folders were used, with two different classes of images, each with 498 images. Below, the results are presented, along with the confusion matrix in Figure 12 and ROC AUC scores in Figure 13. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Comparison** & **T-Statistic** & \(p\)**-Value** & **Statistical Significance** \\ \hline Accuracy & RF vs. GB & 1.633 & 0.1778 & Not significant \\ \hline Accuracy & RF vs. Ridge & 5.222 & 0.0064 & Significant \\ \hline Accuracy & GB vs. Ridge & 4.961 & 0.0077 & Significant \\ \hline Precision & RF vs. GB & 1.521 & 0.2030 & Not significant \\ \hline Precision & RF vs. Ridge & 6.678 & 0.0026 & Significant \\ \hline Precision & GB vs. Ridge & 7.115 & 0.0021 & Significant \\ \hline Recall & RF vs. GB & 1.000 & 0.3739 & Not significant \\ \hline Recall & RF vs. Ridge & 2.743 & 0.0517 & Not significant \\ \hline Recall & GB vs. Ridge & 2.260 & 0.0867 & Not significant \\ \hline F1 & RF vs. GB & 1.633 & 0.1779 & Not significant \\ \hline F1 & RF vs. Ridge & 5.302 & 0.0061 & Significant \\ \hline F1 & GB vs. Ridge & 4.986 & 0.0076 & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of paired \(t\)-tests. **Random Forest Final Evaluation Metrics after Testing:**  
**Accuracy: 81.12%**  
Correct classifications of the model during final testing correspond to a reasonably good performance considering the context of the dataset's class distribution. **Precision: 92.12%**  
During the prediction of an argumentative or non-argumentative instance, the model had a 92.12% probability of correct classification. This suggests that the model is reliable in positive predictions with minimal numbers for false alarms. **Recall: 68.07%**  
From all the actual argumentative and non-argumentative instances, the model correctly identified 68.07% of the true instances. The remaining 31.93% of instances were false negatives. **F1 Score: 78.29%**  
This score reflects a good balance between recall and the precision scores of the model, also highlighting the potential for improvement. Figure 12: Confusion Matrix of Random Forest Classifier after testing. Figure 13: ROC AUC score of Random Forest Classifier after testing. Positive Predictive Value (PPV): 0.9212  
This translates to the classification by the Random Forest model being correct 92% of the time. Negative Predictive Value (NPV): 0.7468  
The classification of a negative instance is correct 74% of the time. True Positives (TPs): 339--Instances of the positive class correctly identified by the model. True Negatives (TNs): 469--Instances of the negative class correctly identified by the model. False Positives (FPs): 29--Instances of the negative class incorrectly identified by the model. False Negatives (FNs): 159--Instances of the positive class incorrectly identified by the model. Below are the final evaluation metrics of the final model after testing:  
Accuracy for Argumentative Detections: 68.07%  
From 498 images with true labels as argumentative, the model only identified 68.07% of them as actual argumentative. Accuracy for Non-Argumentative Detections: 94.18%  
From 498 images with true labels as non-argumentative, the model only identified 94.18% of them as actual non-argumentative. The ROC curve in Figure 13 indicates that the model has excellent power for discriminating between different classes (in our case, argumentative and non-argumentative behaviors). The value of **0.9462** suggests that our model can distinguish between the two classes correctly **94.62%** of the time. Further, this illustrates how the model performs at various decision thresholds.