## 3 Results
### Trained Model Performance  
The best model in our case was considered the model with the highest F1 score. Cross-validation was limited to 5 folds in order to avoid overfitting issues and to correctly calculate each model's general performance. The results are as shown in Tables 1 and 2 and in Figure 3:  
**Random Forest Cross-Validation Metrics:**  
**Accuracy: 1.00 (+/\(-\)0.01);**  
**Precision: 0.99 (+/\(-\)0.02);**  
**Recall: 1.00 (+/\(-\)0.00);**  
**F1 Score: 1.00 (+/\(-\)0.01). **  
**Gradient Boosting Cross-Validation Metrics:**  
**Accuracy: 0.99 (+/\(-\)0.02);**  
**Precision: 0.98 (+/\(-\)0.04);**  
**Recall: 1.00 (+/\(-\)0.01);**  
**F1 Score: 0.99 (+/\(-\)0.02). **Even though the Gradient Boosting performance was exemplary, with similar results to the Random Forest Classifier, the preferred model was Random Forest due to its higher F1 score. Random forest exhibits a better balance between precision and recall making it more reliable. Also, Gradient boosting appears more likely to identify true positives, therefore maximizing recall with a cost in precision value. Following the best evaluated model selection (Random Forest), performance metrics were calculated using the test set as shown in Table 1.  
**Classification Performance on Test Set using Random Forest:**  
The Random Forest classifier exhibits excellent performance on the test set, with nearly perfect scores in all metrics [39]. The Test-Set Final Random Forest Cross-Validation Metrics are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Class 0 (Non-Argumentative) & 1.00 & 1.00 & 1.00 & 89 \\ \hline Class 1 (Argumentative) & 1.00 & 1.00 & 1.00 & 98 \\ \hline Accuracy & & & 1.00 & 187 \\ \hline Macro avg & 1.00 & 1.00 & 1.00 & 187 \\ \hline Weighted avg & 1.00 & 1.00 & 1.00 & 187 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance of the Random Forest classification model. Figure 3: Cross-validation metrics for the three models. In addition, this high level of performance could indicate that the specific dataset used for this method aids towards developing this classifier and also that the dataset provides clear distinction between classes. As stated previously, methods preventing potential overfitting were used such as avoiding wrong imputation methods, advanced hyperparameter fine-tuning and unbalanced dataset usage. However, the final results could indicate possible overfitting. To mitigate this, cross-validation was used. Nonetheless, the derived classification model should be tested on different datasets, furthering the understanding of the model's generalization [31]. Below are the learning curves for all three models, displaying performance and loss during training (see Figure 4). Random Forest and Gradient Boosting perform perfectly in classifying the positive and negative classes (Argumentative and Non-Argumentative), indicating that the model is able to identify all cases of argumentative and non-argumentative behaviors correctly and without errors. Ridge classifier's AUC value of 0.99 is exceptionally high, showing that it is also performing with almost perfect accuracy. Various techniques to control overfitting such as train-test split, cross-validation and confusion matrix evaluation are embedded in the code used to develop the models. The models display the ability to generalize effectively on the data based on the low standard deviations across the folds combined with the high performance metrics. However, in the presence of a potential overfitting risk due to the controlled setting of the dataset used, there is still the need to address some cases which may not be representative of a more diverse real-world scenario. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Accuracy & \(1.00\) \\ \hline Precision & \(1.00\) \\ \hline Recall & \(1.00\) \\ \hline F1-Score & \(1.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cross-validation evaluation metrics.