## 5 Results
### Factor Analysis through XGBost and Logistic Regression  
In XGBost, there are three hyper-parameters to be first optimized to prevent overfitting and increase accuracy. Table 6 lists the results of the hyper-parameter optimization. We have observed that when the learning rate is 0.1, the depth of the tree is 3, and the number of weak learners is 200, it achieves the highest accuracy, which is 68.95 %. However, the difference in accuracy according to hyper-parameter changes is not large. In general, hyper-parameter tuning is performed to prevent overfitting or underfitting the model in order to find accurate trends in the dataset. The reason that hyper-parameter optimization does not dramatically change the results is that the dataset has a clear tendency. Comparing Table 7 with Tables 3-5 shows that the victim's vehicle type = pedestrian, violation of law = signal violation, and victim's vehicle type = two-wheeler are considered important variables in judging serious accidents, while victim's vehicle type = passenger car and perpetrator's vehicle type = passenger car are considered important variables in determining slight accidents. In particular, the victim's vehicle type = pedestrian was chosen as the most important variable in determining the severity of traffic accidents. In logistic regression, we need to choose L1 or L2 regularization and optimize the C value, which adjusts the degree of the fitting. Table 8 shows the results of the hyper-parameter optimization. Based on the experimental results, the C value was set to be 1 with L1 regularization. As in the case of XGBoost, even in logistic regression, the difference in accuracy according to hyperparameter changes is not large. This again supports the clear trend of the dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**Parameters**} & \multicolumn{1}{c}{**Accuracy \%**} \\ \hline
**Learning rate** & **Estimators** & **Max Depth** & \\ \hline \multirow{6}{*}{**0.1**} & & 3 & 68.76 \\  & & 100 & 5 & 68.69 \\  & & 7 & 68.19 \\  & & **3** & **68.95** \\  & **200** & 5 & 68.42 \\  & & 7 & 67.71 \\ \hline \multirow{6}{*}{0.2} & & 3 & 68.90 \\  & 100 & 5 & 68.54 \\ \cline{1-1}  & & 7 & 67.63 \\ \cline{1-1}  & & 3 & 68.76 \\ \cline{1-1}  & 200 & 5 & 67.64 \\ \cline{1-1}  & & 7 & 66.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of hyper-parameters of XGBoost. \begin{table}
\begin{tabular}{c c} \hline \hline
**Independent Variables** & **F Score** \\ \hline Victim’s vehicle type = Pedestrian & 53 \\ Victim’s vehicle type = Passenger car & 48 \\ Perpetrator’s vehicle type = Passenger car & 47 \\ Violation of law = Signal violation & 46 \\ Victim’s vehicle type = Two-wheeler & 46 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Importance of independent variables. \begin{table}
\begin{tabular}{c c} \hline \hline
**Independent Variables** & **F Score** \\ \hline Victim’s vehicle type = Pedestrian & 53 \\ Victim’s vehicle type = Passenger car & 48 \\ Perpetrator’s vehicle type = Passenger car & 47 \\ Violation of law = Signal violation & 46 \\ Victim’s vehicle type = Two-wheeler & 46 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Effects of hyper-parameters of Logistic Regression. Table 9 shows the top 10 regression coefficients. A higher value means a higher influence on the severity of traffic accidents. The result shows that whether the perpetrator is speeding has the most significant impact and that the victim's vehicle type has a significant impact on serious traffic accidents for the case of a motorized bicycle, pedestrian, bicycle, and elderly victim. Additionally, when the perpetrator's vehicle type is a two-wheeler, it has a high impact on the severity of traffic accidents, supporting the claim that motor-cyclists are more likely to be seriously injured in a traffic crash than people in passenger cars.