## 6 Discussion  
In the case of predicting bid risks by using text data on pre-bid clarification, which represents the uncertainty of the bidding document, it is confirmed that prediction performance is greater than when predicted using numeric data only. In other words, the uncertain risk factors in the bidding document of each project obtained from the pre-bid RFI text data have a positive effect in increasing the accuracy of the risk prediction model. This means that unstructured text data contains useful information for predicting bid risks. As a result, the information in the bidding document, which is an unstructured text data, is a major consideration when calculating the bidders' bid price. Furthermore, the results of this study enable inferences to be made about the type of uncertainty in the bidding document that increases the project's risk. That is, if the \"quantity and cost of the rock excavation work\" are not explicitly stated in the bid document, the bid risk can be increased. Moreover, when reviewing the bidding document for the infrastructure projects, uncertain information about the \"rock excavation work\" need to be clarified in advance through the pre-bid clarification process. In addition, the precision and recall of the high risk group were higher than those of the other two groups. This means that the high risk group can be clearly distinguished from the other two groups. The reason why the high risk group has the highest prediction accuracy can be inferred from the text topic modeling results of each risk group. The topic of \"excavation quantity and payment (topic 4)\", which most frequently occurred in the pre-bid RFI of the projects belonging to the high risk group, was rarely occurred in the other two groups. However, the topic 9 and topic 7, which appeared in the moderate risk group and the low risk group, respectively, occurred in similar frequency in the other groups. As a result, the risk factors included in the high risk group projects were highly predictive because they are exclusive, unlike the other two groups. In this study, risk prediction modeling based on uncertainty risk, which represents project characteristics, was conducted at the bidding stage of construction projects. However, various \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Classification Algorithm**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Class Precision**} & \multicolumn{4}{c}{**Class Recall**} \\ \cline{3-8}  & & **A** & **B** & **C** & **A** & **B** & **C** \\ \hline ANNs & 70.83\% & 63.6\% & 58.3\% & 92.9\% & 77.8\% & 46.7\% & 86.7\% \\ SVM & 72.92\% & 63.6\% & 63.6\% & 93.9\% & 77.8\% & 46.7\% & 93.3\% \\ KNN & 70.83\% & 64.7\% & 53.3\% & 93.8\% & 61.1\% & 53.3\% & 100\% \\ NB & 70.83\% & 71.4\% & 57.9\% & 86.7\% & 55.6\% & 73.3\% & 86.7\% \\ \hline \end{tabular} Note: A = low risk group; B = moderate risk group; C = high risk group.  
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Table 4: Results of classification with integrated data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Classification Algorithm**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Class Precision**} & \multicolumn{4}{c}{**Class Recall**} \\ \cline{3-8}  & & **A** & **B** & **C** & **A** & **B** & **C** \\ \hline ANNs & 45.83\% & 55.6\% & 41.7\% & 38.9\% & 55.6\% & 33.3\% & 46.7\% \\ SVM & 52.08\% & 57.9\% & 42.9\% & 53.3\% & 61.1\% & 40\% & 53.3\% \\ KNN & 50\% & 52.6\% & 46.2\% & 50\% & 55.6\% & 40\% & 53.3\% \\ NB & 45.83\% & 50\% & 333\% & 53.3\% & 50\% & 33.3\% & 53.3\% \\ \hline \end{tabular} Note: A = low risk group; B = moderate risk group; C = high risk group.  
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Table 3: Results of classification with numeric data only.