## 4 Experimental Results
### Feature Analysis
#### 4.3.1 Feature Importance  
The following shows the importance of the variables that worked mainly in differentiating the mileage interval. In the random forest algorithm, the importance of variables is derived by incNodePurity, which is measured by an impurity of nodes that is reduced after branching a tree by a specific variable. The impurity is calculated by the summation of the residual products of each node. The importance of the variables is visualized in Figure 5. The first figure represents the feature  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{2}{c}{**Actual**} & \multirow{2}{*}{**Class Error**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-4}  & **60,000 km** & & & \\ \hline
60,000 km & 36 & 5 & 0.12 & \\
70,000 km & 7 & 14 & 0.33 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics of mileage interval detection (60,000 vs. 70,000 km).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{2}{c}{**Actual**} & \multirow{2}{*}{**Class Error**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-4}  & **30,000 km** & & & \\ \hline
30,000 km & 24 & 8 & 0.25 & \\
50,000 km & 1 & 90 & 0.01 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics of mileage interval detection (30,000 vs. 50,000 km).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{2}{c}{**Actual**} & \multirow{2}{*}{**Class Error**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-4}  & **30,000 km** & & & \\ \hline
30,000 km & 24 & 8 & 0.25 & \\
50,000 km & 1 & 90 & 0.01 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics of mileage interval detection (50,000 vs. 60,000 km).  
importance in recognizing the change of mileage interval from 30,000 to 50,000 km. In this case, 'intake air temperature mean' has the highest value in importance. The scree point is found when the slope of importance decrease has changed as features are arranged according to the Gini index (the intercept point between the orange and the blue lines in Figure 5a). Key features from the highest Gini index to the point of this scree point are selected.  
Figure 5: Ten minute basis feature importance of vehicle parts according to mileage interval changes: (**a**) 30,000 vs. 50,000 km; (**b**) 50,000 vs. 60,000 km; (**c**) 60,000 vs. 70,000 km.  
Table 7 summarizes the important variables with high Gini index before the scree point. The results indicate that the characteristics of vehicle parts that significantly change depend on the mileage interval. When the mileage interval shifts from 30,000 to 50,000 km, the intake air temperature and inverter temperature for MG1 and ambient temperature, are mainly changed. For the shift of mileage interval from 50,000 to 60,000 km, ambient temperature no longer exhibits differences, but the intake air temperature and inverter temperature for MG1 show big differences between two intervals. For the shift from 60,000 to 70,000 km, the changes in the auxiliary battery voltage and the inhaling air temperature are newly observed.  
The analysis results show that the major temperature-related variables are closely related to the mileage interval. This is because the change in mileage interval reflects the characteristics of the driving environment, such as the atmospheric temperature according to the season. This is a self-evident result in real driving situations.  
At the same time, it also clearly shows that the temperature change characteristics of the system and module by the thermal management strategy are linked to mileage. This is because the stresses of automotive parts and modules are related to temperature and the thermal management system controls the temperature in response. Since the voltage of the battery is affected by aging and thermal management of the battery, it is obvious that the battery voltage is also affected.