## 3 Results
### Trained Model's Performance
#### 3.1.1 Classification Performance in the Training Set Using Random Forest
**Recall:** This is the percentage of the actual positive cases that the model correctly identifies. The score of 0.93 means that the model successfully identifies 93.75% of the actual positive cases. **F1-Score:** This is the precision and recall's harmonic mean, which balances both metrics in order to identify both false positives and false negatives. The score of 0.93 indicates that the model has both high sensitivity and specificity. **Accuracy:** This is the correctly classified instances' overall percentage. The score of 0.92 shows that the model performs well in the overall classification. Figure 1: ROC-AUC scores of the model after training. A model that makes random guesses (practically a model with no discriminative power), is represented by the diagonal dashed blue line that extends from the bottom left (0, 0) to the top right (1, 1). The ROC curve for any model that outperforms the random one will be above this diagonal line. **Positive Predictive Value (PPV):** This is the percentage of actually positive instances out of all the instances the model evaluated as positive. The score of 0.93 shows that most of the positive predictions are correct. **Negative Predictive Value (NPV):** This is the percentage of actually negative instances out of all the instances the model evaluated as negative. The score of 0.92 shows that the model is predicting negative instances correctly 92% of the time. **Balanced Accuracy After Cross-validation:** The score of 0.92 indicates that the pre cross-validation accuracy and post cross-validation accuracy are consistent, meaning that no significant bias or instability had been introduced to the model, further enhancing the model's reliability. **True Positives (TPs):** 30--Instances of the positive class correctly identified by the model. **True Negatives (TNs):** 23--Instances of the negative class correctly identified by the model. **False Positives (FPs):** 2--Instances of the negative class incorrectly identified by the model. **False Negatives (FNs):** 2--Instances of the positive class incorrectly identified by the model. **Learning Curve:** This depicts the performance of the model during its training with the training dataset. The training score showcases that the model performs extremely well with the training data, as is expected because the data have never been seen by the model. The cross-validation score shows a small gap (0.9-1.0) with the training score, while making a plateau, indicating that the model generalizes well Figure 3.  
Figure 2: Confusion matrix of the model after training. is no significant statistical difference between them. A high \(p\)-value indicates that cross-validation did not change the performance of the model significantly. **T-test for Accuracy:** The \(p\)-value is greater than 0.05, meaning that there is no statistically significant difference between the accuracy of the cross-validation and the accuracy of the test set  
**T-test for Precision:** The \(p\)-value is greater than 0.05, meaning that there is no statistically significant difference between the precision of the cross-validation and the precision of the test set  
**T-test for Recall:** The \(p\)-value is high, meaning that the difference in recall between the cross-validation and the test set is because of random chance, without any statistically significant difference. That also indicates that the test set's recall is not different from the cross-validation's recall. **T-test for the F1-Score:** There is no statistically significant difference between the F1-scores acquired from the cross-validation and the test set, indicating that the F1-score is similar across both datasets.