## 5 Results
### Performance of the Classifiers on the Imbalanced Corpus
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Classifier**} & \multicolumn{1}{c}{**Metric**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**3**} & \multicolumn{1}{c}{**4**} & \multicolumn{1}{c}{**5**} & \multicolumn{1}{c}{**6**} & \multicolumn{1}{c}{**7**} & \multicolumn{1}{c}{**8**} & \multicolumn{1}{c}{**9**} & \multicolumn{1}{c}{**10**} & \multicolumn{1}{c}{**Average**} \\ \hline \multirow{5}{*}{CART} & Recall & 0.640 & 0.772 & 0.678 & 0.722 & 0.615 & 0.723 & 0.707 & 0.611 & 0.668 & 0.652 & 0.679 \\  & Precision & 0.676 & 0.768 & 0.671 & 0.712 & 0.657 & 0.665 & 0.692 & 0.707 & 0.658 & 0.663 & 0.687 \\  & \(F\) measure & 0.657 & 0.770 & 0.675 & 0.717 & 0.635 & 0.693 & 0.699 & 0.655 & 0.663 & 0.657 & 0.682 \\  & ROC-AUC & 0.803 & 0.873 & 0.818 & 0.842 & 0.789 & 0.841 & 0.836 & 0.791 & 0.815 & 0.808 & 0.822 \\  & PR-AUC & 0.676 & 0.781 & 0.693 & 0.733 & 0.656 & 0.708 & 0.713 & 0.679 & 0.680 & 0.674 & 0.699 \\ \hline \multirow{5}{*}{Compl. Naive} & Recall & 0.231 & 0.272 & 0.229 & 0.231 & 0.230 & 0.277 & 0.287 & 0.200 & 0.251 & 0.242 & 0.245 \\  & Precision & 0.977 & 0.980 & 1.000 & 0.980 & 0.956 & 0.927 & 1.000 & 1.000 & 0.979 & 0.956 & 0.976 \\  & \(F\) measure & 0.374 & 0.426 & 0.373 & 0.374 & 0.371 & 0.427 & 0.446 & 0.333 & 0.400 & 0.386 & 0.391 \\  & ROC-AUC & 0.929 & 0.961 & 0.941 & 0.942 & 0.932 & 0.925 & 0.941 & 0.930 & 0.946 & 0.936 & 0.938 \\  & PR-AUC & 0.759 & 0.831 & 0.799 & 0.813 & 0.748 & 0.731 & 0.812 & 0.774 & 0.802 & 0.751 & 0.782 \\ \hline \multirow{5}{*}{\(k\)NN} & Recall & 0.091 & 0.130 & 0.137 & 0.161 & 0.091 & 0.038 & 0.083 & 0.221 & 0.064 & 0.101 & 0.102 \\  & Precision & 1.000 & 0.889 & 0.966 & 1.000 & 0.895 & 0.700 & 0.882 & 0.955 & 0.923 & 0.947 & 0.916 \\  & \(F\) measure & 0.167 & 0.227 & 0.239 & 0.116 & 0.165 & 0.072 & 0.152 & 0.359 & 0.120 & 0.183 & 0.180 \\  & ROC-AUC & 0.927 & 0.952 & 0.915 & 0.950 & 0.931 & 0.905 & 0.928 & 0.929 & 0.919 & 0.928 & 0.928 \\  & PR-AUC & 0.797 & 0.849 & 0.813 & 0.873 & 0.769 & 0.719 & 0.766 & 0.826 & 0.776 & 0.804 & 0.801 \\ \hline \multirow{5}{*}{Random Forest} & Recall & 0.559 & 0.663 & 0.556 & 0.580 & 0.572 & 0.582 & 0.569 & 0.532 & 0.572 & 0.579 & 0.576 \\  & Precision & 0.920 & 0.946 & 0.919 & 0.976 & 0.892 & 0.892 & 0.963 & 0.935 & 0.964 & 0.936 & 0.934 \\  & \(F\) measure & 0.696 & 0.780 & 0.693 & 0.728 & 0.697 & 0.704 & 0.715 & 0.678 & 0.718 & 0.715 & 0.712 \\  & ROC-AUC & 0.967 & 0.979 & 0.961 & 0.971 & 0.950 & 0.951 & 0.968 & 0.963 & 0.964 & 0.963 & 0.964 \\  & PR-AUC & 0.849 & 0.896 & 0.855 & 0.885 & 0.813 & 0.814 & 0.878 & 0.832 & 0.870 & 0.848 & 0.854 \\ \hline \multirow{5}{*}{SVM} & Recall & 0.575 & 0.668 & 0.522 & 0.547 & 0.561 & 0.560 & 0.547 & 0.511 & 0.604 & 0.562 & 0.566 \\  & Precision & 0.955 & 0.939 & 0.973 & 0.943 & 0.938 & 0.904 & 0.952 & 0.960 & 0.950 & 0.943 & 0.946 \\  & \(F\) measure & 0.718 & 0.781 & 0.679 & 0.693 & 0.702 & 0.691 & 0.695 & 0.667 & 0.739 & 0.740 & 0.707 \\  & ROC-AUC & 0.963 & 0.979 & 0.949 & 0.976 & 0.947 & 0.947 & 0.967 & 0.954 & 0.955 & 0.962 & 0.960 \\  & PR-AUC & 0.831 & 0.903 & 0.867 & 0.890 & 0.827 & 0.785 & 0.872 & 0.842 & 0.865 & 0.863 & 0.855 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metrics calculated through 10-fold cross-validation for the five classifiers. * According to Table 2, \(k\)NN (with that setting) seems to be the less fit for classifying this dataset (with the worst values in \(F\)-measure and recall). From this experiment, we identified SVM and Random Forest as the most promising classifiers for this case study. Consequently, we decided to employ sampling algorithms before training in order to enhance the skillfulness of these classifiers.