## 3 Results and Discussion
Bagging had a performance very similar to the RF classifier with a mean AUROC value of 0.9980. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**ANN No.**} & **ANN Profile (inputs:hidden** & \multicolumn{6}{c}{**Sh\({}_{\text{k}}\)(ANN)**} \\ \cline{2-7}  & **layers EPs:outputs** & **k = 0** & **k = 1** & **k = 2** & **k = 3** & **k = 4** & **k = 5** \\ \hline
1 & MLP15:15-14-1:1 & 0.05 & 0.054 & 0.054 & 0.054 & 0.054 & 0.054 \\
2 & MLP4:4-6-11-1:1 & 0.06 & 0.067 & 0.069 & 0.072 & 0.072 & 0.072 \\
3 & MLP5:5-8-1:1 & 0.078 & 0.088 & 0.097 & 0.097 & 0.097 & 0.097 \\
4 & MLP7:7-11-1:1 & 0.067 & 0.074 & 0.081 & 0.081 & 0.081 & 0.081 \\
5 & MLP9:9-12-1:1 & 0.061 & 0.067 & 0.071 & 0.071 & 0.071 & 0.071 \\
6 & MLP10:10-12-1:1 & 0.061 & 0.067 & 0.071 & 0.071 & 0.071 & 0.071 \\
7 & MLP4:4-8-11-1:1 & 0.057 & 0.059 & 0.06 & 0.061 & 0.061 & 0.061 \\
8 & MLP10:10-11-12-1:1 & 0.046 & 0.048 & 0.044 & 0.046 & 0.046 & 0.046 \\
9 & LNN14:14-1:1 & 0.056 & 0.146 & 0.146 & 0.146 & 0.146 & 0.146 \\
10 & LNN15:15-1:1 & 0.053 & 0.146 & 0.146 & 0.146 & 0.146 & 0.146 \\ \hline \end{tabular}
\end{table} 
Table 1: Information indices Sh\({}_{\text{k}}\)(ANN) of the ANNs used as inputs to find the best Net-Net Automated Machine Learning (AutoML) model. In the next step, we performed a grid search for the RF number of trees: 1 tree (DT), and 2, 5, 10, 50, 100 trees (RF2, RF5, RF10, RF50, RF100). The results show that by using two trees with Random Forest, it is possible to increase the mean AUROC value from 0.9925 to 0.9944 (DT) (see Table 3 and Figure 3). By doubling the number of trees from 50 to 100, the difference between RF50 and RF100 for the mean AUROC value was just 0.0001. Therefore, we chose the best Net-Net AutoML model as RF50 with a mean AUROC value of 0.9991 (SD = 0.0001). The dataset, scripts, and results are available as an open GitHub repository [63]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Fold** & **KNN** & **LDA** & **GNB** & **SVM\_RBF** & **LogR** & **MLP** & **DT** & **RF101** & **XGB** & **GB** & **Ada** & **Bagging** \\ \hline
1 & 0.9956 & 0.9195 & 0.9164 & 0.9625 & 0.9510 & 0.9876 & 0.9926 & 0.9983 & 0.9923 & 0.9937 & 0.9652 & 0.9983 \\
2 & 0.9955 & 0.9217 & 0.9198 & 0.9642 & 0.9529 & 0.9879 & 0.9924 & 0.9982 & 0.9923 & 0.9933 & 0.9662 & 0.9980 \\
3 & 0.9957 & 0.9203 & 0.9193 & 0.9637 & 0.9524 & 0.9880 & 0.9920 & 0.9983 & 0.9919 & 0.9934 & 0.9659 & 0.9980 \\
4 & 0.9962 & 0.9207 & 0.9192 & 0.9637 & 0.9525 & 0.9851 & 0.9927 & 0.9981 & 0.9926 & 0.9938 & 0.9660 & 0.9982 \\
5 & 0.9957 & 0.9204 & 0.9204 & 0.9645 & 0.9538 & 0.9882 & 0.9924 & 0.9983 & 0.9926 & 0.9934 & 0.9662 & 0.9980 \\
6 & 0.9960 & 0.9192 & 0.9190 & 0.9633 & 0.9518 & 0.9885 & 0.9925 & 0.9982 & 0.9925 & 0.9936 & 0.9656 & 0.9979 \\
7 & 0.9958 & 0.9219 & 0.9205 & 0.9645 & 0.9535 & 0.9888 & 0.9925 & 0.9983 & 0.9926 & 0.9940 & 0.9669 & 0.9982 \\
8 & 0.9955 & 0.9215 & 0.9196 & 0.9642 & 0.9529 & 0.9884 & 0.9927 & 0.9981 & 0.9925 & 0.9940 & 0.9664 & 0.9978 \\
9 & 0.9959 & 0.9176 & 0.9183 & 0.9625 & 0.9514 & 0.9875 & 0.9922 & 0.9982 & 0.9923 & 0.9933 & 0.9646 & 0.9979 \\
10 & 0.9964 & 0.9182 & 0.9168 & 0.9623 & 0.9508 & 0.9879 & 0.9933 & 0.9984 & 0.9922 & 0.9933 & 0.9651 & 0.9982 \\ \hline
**Mean** & **0.9958** & **0.9201** & **0.9189** & **0.9635** & **0.9523** & **0.9878** & **0.9925** & **0.9983** & **0.9924** & **0.9936** & **0.9658** & **0.9980** \\
**SD** & **0.0003** & **0.0015** & **0.0014** & **0.0009** & **0.0010** & **0.00010** & **0.0003** & **0.0001** & **0.0002** & **0.0003** & **0.0007** & **0.0002** \\ \hline \hline \end{tabular}  
* RF10 = Random Forest with 10 trees.  
\end{table} 
Table 2: AUROC for Net-Net AutoML classification models. Figure 2: Box plot for the AUROC values of 12 Net-Net AutoML classification models. Figure 4 shows the feature importance for the best Random Forest model based on 50 trees. We can observe that the entropies of the BCN nodes are the most important features followed by the ANN entropies.