## 4 Results
### Results on the Test-Set  
In this section, the results obtained on the Test-Set by the various architectures will be presented. The metrics used to express the results include Accuracy, Precision, Recall, and F1-Score. The results are shown in Table 2.  
From the table, it is possible to analyze the results of the various architectures:  
* Single-Layer Perceptron: It achieved relatively stable base performance in terms of Accuracy but shows some difficulty in generalizing to the test data. Precision is very low, suggesting that the model struggles to accurately distinguish positive classes, while Recall remains relatively high, at the expense of Precision. The F1-Score reaches modest values, confirming the limitations of the architecture. * Shallow Network: There is a significant improvement compared to the previous architecture. Accuracy reaches good values, indicating an enhanced ability of the model to generalize to unseen data. Precision also improves significantly, suggesting that the model is more accurate in correctly distinguishing positive classes compared to the previous architecture. Recall remains high, indicating that the model maintains a strong ability to identify most positive examples. The overall improvement is confirmed by the increase in the F1-Score, which reaches a good value. This highlights the need to use models capable of capturing nonlinear relationships to address the problem and ensure better generalization. * Random Forest: It achieves excellent Accuracy, indicating that the model has a good ability to generalize to unseen data. Precision reaches the best results among all the architectures, indicating that the model is very effective in reducing false positives, successfully distinguishing positive examples. However, Recall shows a slight decrease, demonstrating a good ability to correctly identify positive examples, but with a slight drop compared to Precision. The F1-Score confirms the model's good ability to balance the correct classification of positive examples with the reduction in false positives, achieving slightly lower but very similar results to the previous architecture. * Long Short-Term Memory: It achieves excellent Accuracy, indicating a very good generalization ability on unseen data. Precision reaches fairly high values, demonstrating that the architecture is effective in reducing false positives and accurately distinguishing positive examples. Recall remains high, indicating that the model possesses a strong ability to identify the majority of positive examples. Finally, the F1-Score \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Single-Layer Perceptron & 0.7681 & 0.3636 & 0.8 & 0.5 \\ \hline Shallow Network & 0.9347 & 0.7619 & 0.8 & 0.7804 \\ \hline Random Forest & 0.9420 & 0.875 & 0.7 & 0.7777 \\ \hline Long Short-Term Memory & 0.9492 & 0.8421 & 0.8 & 0.8205 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test-Set results for all models. reaches the highest values among all models, confirming the model's ability to balance the correct classification of positive examples with the reduction in false positives. The results clearly show that the LSTM model achieves the best overall performance, leveraging its ability to capture long-term relationships and use them for more accurate decision making. In particular, Accuracy is the highest among the models, indicating excellent capability in correctly classifying samples. The Random Forest and Shallow Network follow closely behind, with values 0.76% and 1.55% lower, respectively. Regarding Precision, the LSTM achieves a good score, demonstrating solid capability in reducing false positives. The Random Forest stands out particularly in this metric, achieving a result 3.91% higher than the LSTM and 14.8% higher than the Shallow Network, while the Single-Layer Perceptron exhibits significantly lower values.