## 3 Results  
### Classification \"Driving Style\"  
The classification results for the \"driving style\" attribute reveal insightful findings about the performance of various machine learning models. In our study, we focused on two primary categories of driving styles: even pace style and aggressive style. The random forest (RF) classifier achieved a commendable accuracy of 94.67%, indicating its proficiency in identifying driving styles. The precision-recall balance was particularly impressive in classifying the predominant class, with a sensitivity of 99.05%, demonstrating the model's capability to correctly identify aggressive driving styles. The specificity remained at 59.35%, indicating moderate accuracy in recognizing even-paced driving. Show Table 2.  
On the other hand, the decision tree (DT) classifier showed an accuracy of 91.61%. This model had a more balanced performance between the classes, with a sensitivity (recall) for the aggressive driving style at 95.05% and a specificity for the even-paced style at 63.88%. The f1-scores indicate a fair balance between precision and recall, although slightly lower than the random forest model. The K-nearest neighbors (KNN) classifier's accuracy was slightly lower at 90.99%. It demonstrated a higher precision in predicting aggressive driving style, with a sensitivity of 96.24%. However, the even-paced driving style had a lower specificity of 48.64%, indicating some challenges in accurately identifying this class. Lastly, the ensemble of models using a voting classifier achieved an accuracy of 93.89%. This approach, which combined the strengths of the individual models, resulted in a high sensitivity of 97.93% for the aggressive driving style, signifying a high rate of correctly identifying this class. The specificity was 61.34% for the even-paced driving style, which is an improvement over the KNN model but still suggests room for enhancement. All of these results show that ensemble methods are good at finding the right balance between sensitivity and specificity, which leads to high overall accuracy in classification tasks. The RF model stood out with the highest accuracy and recall, indicating its strong performance in this particular scenario. The ensemble approach proved to be a robust strategy, offering a competitive alternative to individual model predictions. The two figures provided represent the feature contributions to individual sample predictions using a random forest model, as interpreted by the tree interpreter tool. Both figures visually break down how each feature influences the model's prediction of a particular class for two different samples from the dataset. Figure 6 provides a visual depiction of the individual feature contributions to the random forest model's prediction for Sample 3. The random forest model measures and displays each feature's impact as either a positive or negative contribution towards the positive class prediction, represented by blue and red bars, respectively. In this figure, the length of each bar signifies the magnitude of the features' influence on the model's prediction. For example,'vehicle speed instantaneous' and'vehicle peed average' are shown with long blue bars, indicating strong positive contributions. This suggests that for Sample 3, when these speed-related attributes have higher values, they are influential indicators that the model associates with the positive class, which could be, for instance, an 'Aggressive Driving Style'. On the other hand, features like 'Engine RPM' and 'Mass Air Flow' exhibit red bars, denoting negative contributions. This implies that higher readings of these particular features sway the model in favor of predicting the negative class. In the context of driving style, this could mean that when the engine operates at higher RPMs, or there is greater mass air flow, the driving style may align more closely with 'Even Paced' rather than 'Aggressive' driving.