## 3 Results
### Base Model Metrics  
Figure 7 illustrates a comparative picture of the overall accuracy metric for the different classifiers implemented in the base model (unbalanced) during the training phase.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Grouping Range** & **Frequency** & **\%** \\ \hline Low & Values \(\leq\) 64 & 40 & 21.5 \\ Medium & Values 64–76 & 112 & 60.2 \\ High & Values \(\geq\) 76 & 34 & 18.3 \\ \hline \hline \end{tabular} Note. Own elaboration.  
\end{table}
Table 13: Percentage of sustainability level by grouping ranges.  
Figure 6: Overall sustainability index of the sample of projects based on the input consensus. Note. Own elaboration.  
The best classifiers are in the following order: SVM \((0.906\pm 0.047)\); KNN\((0.897\pm 0.057)\); LR\((0.872\pm 0.045)\); RF\((0.861\pm 0.069)\).  
We can see that, in most of the classifiers, both the mean and the median are very close together, suggesting a certain symmetry and stability of the indicator distribution.  
However, for unbalanced data, conclusions cannot be drawn from the overall accuracy metric alone [63; 64], as it may not be considering the minority classes; therefore, in these circumstances, we must look for other types of metrics that can provide greater reliability in the interpretation of the indicators.  
Table 14 shows the metrics obtained in the testing phase for the best classifiers. As can be seen, the order of importance of the classifiers is the same as that obtained during the training phase. It can be observed that the accuracy values achieved for each particular class are quite good.  
In general, high values of precision and recall were obtained, indicating that the model generalizes said class perfectly. However, for the Random Forest model, maximum precision and low recall were obtained for the low class, suggesting that this model does not detect said class well, but, when it does, it is highly reliable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Classifier** & **Class** & **tp** & **tn** & **fp** & **fn** & **Accuracy** & \
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & **Precision** & **Recall** &
\begin{tabular}{c} **F1** \\ **Score** \\ \end{tabular} & **ROC/AUC** \\ \hline \multirow{3}{*}{LR} & High & 5 & 29 & 2 & 2 & 0.89 & & 0.71 & 0.71 & 0.98 \\  & Low & 6 & 30 & 0 & 2 & 0.95 & 0.84 & 1.00 & 0.75 & 0.86 & 1.00 \\  & Medium & 21 & 11 & 4 & 2 & 0.84 & & 0.84 & 0.91 & 0.87 & 0.97 \\ \hline \multirow{3}{*}{SVM} & High & 6 & 30 & 1 & 1 & 0.95 & & 0.86 & 0.86 & 0.86 & 0.98 \\  & Low & 6 & 30 & 0 & 2 & 0.95 & 0.89 & 1.00 & 0.75 & 0.86 & 1.00 \\  & Medium & 22 & 12 & 3 & 1 & 0.89 & & 0.88 & 0.96 & 0.92 & 0.97 \\ \hline \multirow{3}{*}{RF} & High & 5 & 30 & 1 & 2 & 0.92 & & 0.83 & 0.71 & 0.77 & 0.98 \\  & Low & 3 & 30 & 0 & 5 & 0.87 & 0.79 & 1.00 & 0.38 & 0.55 & 1.00 \\  & Medium & 22 & 8 & 7 & 1 & 0.79 & & 0.76 & 0.96 & 0.85 & 0.94 \\ \hline \multirow{3}{*}{KNN} & High & 6 & 29 & 2 & 1 & 0.92 & & 0.75 & 0.86 & 0.80 & 0.99 \\  & Low & 5 & 30 & 0 & 3 & 0.92 & 0.84 & 1.00 & 0.62 & 0.77 & 0.88 \\  & Medium & 21 & 11 & 4 & 2 & 0.84 & & 0.84 & 0.91 & 0.87 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Set of metrics of the unbalanced models (testing phase).  
Figure 7: Comparison of the overall accuracy metric for different classifiers during the training phase of the unbalanced model. Note: The orange horizontal line refers to the median and the green triangle to the mean of the indicator.