## 4 Cost Prediction Performance Analysis
### Cost Prediction Test  
Previously, the predictive performance of construction costs for six cases was compared using K-fold. Two final models were determined by combining models that have shown relatively good performance in previous case analyses. Min-Max and Z-Score scaling were applied, respectively, and L2 regularization was applied equally. Then, construction cost prediction models were generated through the Train-Test-Validation process for these models, and their construction cost prediction performance was analyzed.This time, 500 out of the total 608 data were defined as training data and 108 data were defined as validation data, 15% of the training data was set as test data, and Epoch was set to 1000 to ensure enough machine learning. Min-Max scaling and L2 regularization were applied to Case 1, and Z-Score scaling and L2 regularization were applied to Case 2. The machine learning results of the two cases set in this way are shown in Figure 10.  
For validation data, the result of comparing the difference between the Actual Cost, Total Cost, and the sum of the sub-construction costs is shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Cost Sum**} & \multicolumn{2}{c}{**Total Cost**} \\ \cline{2-5}  & **Mean** & **Std** & **Mean** & **Std** \\ \hline
1 & \(-\)0.22228 & 2.146799 & \(-\)0.06806 & 2.574931 \\
2 & 0.6539 & 0.533562 & 0.806193 & 0.048764 \\
3 & 0.733101 & 0.060671 & 0.796978 & 0.026846 \\
4 & 0.652943 & 0.224519 & 0.647259 & 0.191611 \\
5 & 0.742204 & 0.039971 & 0.8101 & 0.028561 \\
6 & 0.749573 & 0.038138 & 0.821215 & 0.01994 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean and Standard deviation of R-square. Figure 10: Feature scaling of Min-max and Z-score. As shown in Table 6, in the case of predicting the construction cost by the total cost or sum of the sub-construction costs, an error of 10% to 20% occurs, but these errors can be reduced by securing sufficient data and optimizing the parameters. The accuracy of cost estimation can be defined as the difference value between the actual cost and the estimated cost, and the error rate can be calculated as in Equation (12) [27; 28]. \[\text{Error rate}(\%)=\frac{|Actual\ Cost-Estimated\ Cost|}{Actual\ Cost}\times 100 \tag{12}\]  
General construction cost accuracy was evaluated with the above error rate rather than R squared score, and the result value of the case evaluated with the error rate is as shown in Table 7. Error rate tends to be measured with higher accuracy than R squared. In the case of Z-Score scaling, it can be found that the prediction of lower costs is more accurate than the total cost prediction. Table 8 shows the results of calculating the predicted error rate of the sub-construction cost using multioutput as R-square. The accuracy of the construction cost, which accounts for the largest proportion, is the highest, and the error rate of the landscaping and civil engineering construction costs is relatively high. Since these construction projects may have a slightly lower correlation with the characteristics of the building, such as the area of \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sum of Costs Prediction**} & \multicolumn{2}{c}{**Total Cost Prediction**} \\ \cline{2-5}  & **Mean** & **Std** & **Mean** & **Std** \\ \hline Min-Max & 0.7422 & 0.0399 & 0.8101 & 0.0286 \\ Z-Score & 0.7496 & 0.0381 & 0.8212 & 0.0199 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: R-square score of cases. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Mean** & **Std** \\ \hline Min-Max Scaling & Sum of sub-cost & 22.6295 & 22.9334 \\ Total Cost & 19.0345 & 18.3321 \\ Z-Score Scaling & Sum of sub-cost & 16.8073 & 16.3058 \\ Total Cost & 17.6702 & 19.4037 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Error Rate of cost estimation. Figure 11: Cost prediction from total cost and cost sum. the building, it is expected that the error rate can be reduced by adjusting and applying the influencing factors.