## 6 Discussion
As well as the multi-class approach, the total number of observations (objects) in each cell and its percentage are exposed in the confusion matrixes. However, the rows and columns are different. The confusion matrix rows for the multi-class classification problem correspond to the output class (true class), which are 1 (no damage), 2 (medium damage), and 3 (severe damage), while the columns correspond to the target class (predicted class), which are 1 (no damage), 2 (medium damage), and 3 (severe damage). To better visualize the problem regarding the \"All Confusion Matrix\" of Test 8 (the best performance model for the multi-class classification method--first case achieved in this work), as shown in Figure 32, its results characteristics can be seen in Table 5, the parameters and values in which are \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Confusion Matrix Guide—Classification Method**} \\ \hline
**Total Number of** & & & \\
**Objects (n) = 360** & & & \\ \hline Actual (0) & TN = 175 & FP = 4 & a1 = TN + FP = 179 \\ \hline Actual (1) & FN = 5 & TP = 176 & a2 = FN + TP = 181 \\ \hline Predicted Objects (p) & p1 = TN + FN = 180 & p2 = FP + TP = 180 \\ \hline  & & & \\ \hline \multicolumn{2}{c}{**Label 1**} \\ \hline True Negative (TN) & & Predicted no damage: yes, and there is no damage. \\ \hline True Positive (TP) & & Predicted damage: yes, and there is damage. \\ \hline False Negative (FN) & & Predicted no damage: yes, and there is damage. \\ \hline False Positive (FP) & & Predicted damage: yes, and there is no damage. \\ \hline \multicolumn{2}{c}{**Label 2**} \\ \hline Accuracy Index & & What is the percentage of correct results for the classifier? \\ \cline{2-4}  & & (TN + TP)/n = (175 + 176)/360 = 0.975 = 97.5\% \\ \hline \multicolumn{2}{c}{What is the percentage of wrong results for it?} \\ \cline{2-4} Misclassification Index & & (FP + FN)/n = (4 + 5)/360 = 0.025 = 2.5\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{True-Positive Index}} & When it is actually (1), what is the percentage of damage (1) prediction for it? \\ \cline{2-4}  & & TP/a2 = 176/181 = 0.972 = 97.2\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{False-Positive Index}} & When it is actually (0), what is the percentage of damage (1) prediction for it? \\ \cline{2-4}  & & FP/a1 = 4/179 = 0.022 = 2.2\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{True-Negative Index}} & When it is actually (0), what is the percentage of no damage (0) prediction for it? \\ \cline{2-4}  & & TN/a1 = 175/179 = 0.978 = 97.8\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{False-Negative Index}} & When it is actually (1), what is the percentage of no damage (0) prediction for it? \\ \cline{2-4}  & & FN/a2 = 5/181 = 0.028 = 2.8\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{Precision Index (0)}} & When it predicts no damage (0), what is the percentage of correct results for it? \\ \cline{2-4}  & & TN/p1 = 175/180 = 0.972 = 97.2\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{No-Precision Index (0)}} & When it predicts no damage (0), what is the percentage of wrong results for it? \\ \cline{2-4}  & & FN/p1 = 5/180 = 0.028 = 2.8\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{Precision Index (1)}} & When it predicts damage (1), what is the percentage of correct results for it? \\ \cline{2-4}  & & TP/p2 = 85/90 = 0.978 = 97.8\% \\ \hline \multicolumn{2}{c}{\multirow{2}{*}{No-Precision Index (1)}} & When it predicts damage (1), what is the percentage of wrong results for it? \\ \cline{2-4}  & & FP/p2 = 5/90 = 0.022 = 2.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion Matrix Guide—Classification Method. explained [65]. For Test 6, 7, and 8, the network took 13, 30, and 50 epochs (training state plots) to train and validate the model, respectively. When the CE of the validation phase reaches the minimum value, this is considered the best mark of the training phase, because overfitting occurs from that point, similarly to the previous classification problem case.