## 4 Results and Discussion
### Development of ML Models Using Building Register Information
The entire dataset was randomly divided into the training and test sets, and the model performance for the test set was an indicator that represented the model's performance for unknown data. In other words, the ML models were developed with 70% of the collected data using the methods mentioned in Section 4.1. The performance of each ML model was evaluated in further detail using the confusion matrix (Figure 3). The figure shows the confusion matrix of the training and validation datasets used to compare actual and predicted values. The confusion matrix can compare the actual value (rating) for the given input variables with the value (rating) predicted by the ML model for the same input variables. The rows of the matrix indicate the predicted values, and its columns represent the actual values. As the values located in the diagonal cells (row 1, column 1; row 2, column 2; and row 3, column 3) show that the actual value (rating) and predicted value (rating) are identical, they indicate success in prediction through the ML models. The other cells in the matrix show that the rating was underestimated or overestimated. For example, the value placed in row 1, column 2 indicates that the ML model predicted a higher rating (moderate) than the actual rating (low) (overestimation), while the value in row 2, column 1 implies that the ML model predicted a lower rating than the actual rating (moderate) (underestimation). It was found that the ANN optimization ML model developed earlier (classifier) accurately predicted the severe rating compared to other ratings. However, its success rate in predicting the moderate rating was low. The DT optimization ML model (classifier) accurately predicted the severe rating (>80%); however, its success rate in predicting other ratings was significantly low. The KNN optimization ML model (classifier) successfully predicted the severe rating (>50%) based on the validation dataset; however, its prediction success rate for other ratings was approximately 30%. The RF optimization ML model (classifier) successfully predicted the severe rating (approximately 80%) based on the validation dataset; however, its prediction success rate for other ratings was less than 20%. In addition, precision and recall were compared and analyzed to select a classifier learning model based on the values calculated from the confusion matrix. Precision is an indicator that represents the accuracy of prediction, while recall indicates the proportion of the data accurately predicted by the classifier and is an indicator that shows the classifier's sensitivity. Precision and recall have a trade-off relationship wherein an increase in either causes a decrease in the other [37]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & \(\beta\) & **SE** & **F** & \(p\)**-Value** \\ \hline Building location information & 0.080 & 0.024 & 10.646 & \textless{}0.001 \\ Number of floors & 0.047 & 0.037 & 1.578 & 0.209 \\ Number of basement floors & \(-\)0.072 & 0.037 & 3.79 & 0.052 \\ Treating & TFA & \(-\)0.039 & 0.036 & 1.208 & 0.272 \\ Building register information & Building area & \(-\)0.005 & 0.024 & 0.045 & 0.833 \\ Structure & 0.184 & 0.031 & 35.642 & \textless{}0.001 \\ Industry type & 0.115 & 0.022 & 27.132 & \textless{}0.001 \\ Fire-resistant structure & 0.066 & 0.028 & 5.436 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Regression analysis on input variables for output variables (building register information). Figure 3: Confusion matrix of ML models (building register information). For all four models, the precision and recall values for the moderate damage rating could not reach the average values, and those for the low and severe damage ratings were slightly higher than the average values. Additionally, it was found that the recall and precision values were almost similar for all the four models. Since the models were developed for prior response by predicting fire damage using simple information, it is determined that classifiers with higher recall than precision are appropriate.