## 5 Results and Discussion
### Damage Levels Classification Models  
After training the classification models, their performance was evaluated using standard metrics to measure their effectiveness on the test dataset. These metrics include accuracy, precision, recall, and F1 score, as defined based on the confusion charts shown in Figure 11. The diagonal cells, highlighted in various shades of blue, represent the correct predictions for different damage states. In contrast, the off-diagonal cells, highlighted in orange, indicate incorrect predictions. The recall and precision values for each damage state are presented in the final column and row of the chart, respectively, and are marked in grey. It is also important to note that in the seismic assessment of URM buildings, recall values are crucial, as they show the number of samples classified as having reduced damage. This aspect is essential, as misclassifying a sample with Damage State 4 (DS4) as either Damage State 3 (DS3) or Damage State 2 (DS2) may erroneously categorize a severely damaged building as one with moderate or heavy damage. Such misclassification during post-earthquake evaluations could result in a heavily damaged structure being ascribed a lower damage rating, thus posing a significant risk to repair personnel and occupants following an earthquake. The accuracy, training error, and misclassification rates associated with the test sets and the F1-scores of the classification models are listed in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & \(R^{2}\) & **Adjusted \(R^{2}\)** & **RMSE** & \(R^{2}\) & **Adjusted \(R^{2}\)** & **RMSE** \\ \hline Linear Regression (LR) & 0.878 & 0.877 & 0.556 & 0.862 & 0.858 & 0.596 \\ Stepwise Linear Regression (SLR) & 0.877 & 0.876 & 0.558 & 0.860 & 0.857 & 0.601 \\ Ridge Regression (RR) & 0.806 & 0.804 & 0.700 & 0.818 & 0.814 & 0.677 \\ Decision Tree (DT) & 0.966 & 0.966 & 0.289 & 0.923 & 0.921 & 0.476 \\ Random Forest (RF) & 0.978 & 0.978 & 0.226 & 0.945 & 0.943 & 0.410 \\ Support Vector Machine (SVM) & 0.969 & 0.968 & 0.281 & 0.958 & 0.957 & 0.317 \\ Gaussian Process Regression (GPR) & 0.976 & 0.976 & 0.248 & 0.974 & 0.973 & 0.257 \\ Neural Networks (NN) & 0.978 & 0.977 & 0.239 & 0.972 & 0.972 & 0.307 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the prediction models. Figure 8: Actual response and predicted response by the regression models: (**a**) LR, (**b**) SLR, (**c**) RR, (**d**) DT, (**e**) RF, (**f**) SVM, (**g**) GPR, and (**h**) NN. Figure 10: Percentage variations for the (**a**) \(R^{2}\) and (**b**) RMSE values of the training and test sets of the regression models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Misclassification Rate**} & \multirow{2}{*}{**Training Error**} & \multicolumn{5}{c}{**F1 Score**} \\ \cline{5-8}  & & & **DS1** & **DS2** & **DS3** & **DS4** \\ \hline NB & 85.8 & 0.1420 & 0.1098 & 83.86 & 78.92 & 70.03 & 94.52 \\ DA & 83.3 & 0.1667 & 0.1032 & 89.62 & 83.21 & 59.68 & 91.34 \\ KNN & 89.8 & 0.1019 & 0 & 89.62 & 87.06 & 73.69 & 96.10 \\ DT & 88.5 & 0.1142 & 0.0503 & 90.31 & 89.48 & 71.82 & 93.55 \\ RF & 92.9 & 0.0710 & 0 & 93.33 & 91.66 & 81.03 & 96.60 \\ AB & 91.1 & 0.0895 & 0.0013 & 93.33 & 89.05 & 79.37 & 95.79 \\ SVM & 87.7 & 0.1235 & 0.0026 & 71.96 & 85.30 & 73.20 & 94.85 \\ NN & 92.6 & 0.0741 & 0.1323 & 93.33 & 91.04 & 81.03 & 96.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of classification models in identifying the damage state of a URM building. Figure 9: Comparison of results of training and test sets of regression models: (**a**) \(R^{2}\) values and (**b**) RMSE values. Figure 11: Confusion matrices for the classification models in terms of damage state identification: (**a**) NB, (**b**) DA, (**c**) KNN, (**d**) DT, (**e**) RF, (**f**) AB, (**g**) SVM, and (**h**) NN.