## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Feature Selection** & **Classifier** & **AUROC** & **Accuracy Score** \\ \hline From model (LR) & Bagging (SVC) & \(0.82\pm 0.18\) & \(0.78\pm 0.14\) \\ Univariate & SVC & \(0.80\pm 0.13\) & \(0.70\pm 0.15\) \\ Variance threshold & Adaboost & \(0.80\pm 0.14\) & \(0.70\pm 0.15\) \\ From model (LR) & GaussianNB & \(0.79\pm 0.17\) & \(0.74\pm 0.11\) \\ Univariate & ExtraTrees & \(0.79\pm 0.21\) & \(0.76\pm 0.13\) \\ Percentile & AdaBoost & \(0.79\pm 0.27\) & \(0.80\pm 0.19\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Selected pipelines results ordered by AUROC. Figure 6: ROC curves obtained for the selected pipelines. In binary classification models, other metrics may provide valuable information on the performance of a classifier. The precision is defined as a ratio of the number of true positives divided by the sum of true positives and false positives. It is also referred to as the positive predictive power, this is, the ability of the model to predict true positives, which in this work, are the unfavourable cases (GG = 3). The recall (sensitivity) is calculated as the ratio of the number of true positives divided by the sum of true positives and false negatives. The f1-score is the harmonic mean of the precision and recall, while the support is the number of occurrences of each class in the ground truth labels [33]. Table 7 presents these parameters for the selected pipelines and the mean number of features selected in each fold in the cross-validation. The threshold = 0, in the variance threshold method, is not enough to perform any feature selection because the preprocessing standardization removes the mean and scales features to unit variance. The AdaBoost classifier fits additional copies on the same dataset and re-adjusts the weights accordingly. This behaviour may overcome the lack of feature selection when both are combined. Besides the percentile-AdaBoost combination, most pipelines present a low recall value for Class 2 (favourable), returning very few results. Themodel (LR)-bagging (SVC) pipeline has a high precision for Class 3 (unfavourable), suggesting those few results are well classified. This combination, however, has the opposite behaviour for Class 3. A high recall returns many results, but a low precision means those results are poorly classified. The bagging (SVC) classifier aggregates the predictions of several SVCs, reducing the variance of the final output and improving accuracy. When using an SVC classifier, we obtained an accuracy of \(0.0\pm 0.15\), but with a bagging strategy of \(0.78\pm 0.14\). The GaussianNB classifier assumes a Gaussian likelihood with a naive assumption of pair-wise features' conditional independence. By reducing the number of features to 43, we are also eliminating highly correlated features, a factor that degrades the classifier performance. The decision tree creates a piecewise constant approximation to the decision curve, but when using the entire feature set, the set is known to provide some overfitting. The ExtraTrees introduces randomized decision trees on several subsets allowing control of the overfitting and increasing accuracy [33]. percentile feature selection method with an AdaBoost classifier, the obtained results seem to be satisfactory, with relatively high values of precision and recall for both classes. The main advantage of this model is that the number of features used for classification is reduced to 11. This may be a step further in providing explainability and interpretability. In fact, the selected features in each fold of the cross-validation varies, although some are frequently selected. Figure 7 shows the features that were more frequently selected.