## 3 Results
### Classification \"Road Surface\"  
The classification results for the \"road surface\" attribute highlight the effectiveness of the machine learning models employed in distinguishing between different road conditions. The random forest (RF) classifier emerged with an outstanding accuracy of 99.10%, demonstrating its exceptional ability to classify road surface conditions. The precision, recall, and f1-score across all conditions (full of holes condition, smooth condition, and uneven condition) were consistently high, nearly all above 99%. The sensitivity (recall) for each class was also notably high, with Full of Holes Condition at 99.25%, Smooth Condition at 99.47%, and Uneven Condition at 98.14%, indicating the model's strong capability to correctly identify each road surface condition. The specificity values were similarly impressive, indicating the model's accuracy in ruling out the other conditions when identifying a specific one. The decision tree (DT) classifier also performed well, achieving an overall accuracy of 97.88%. While slightly lower than the RF model, the DT classifier maintained high precision, recall, and f1-scores, with macro and weighted averages ranging around 97% to 98%. The sensitivity (recall) for Full of Holes Condition was 97.30%, Smooth Condition was 98.29%, and Uneven Condition was  
Figure 7: Feature contributions for sample 20 in driving style classification. 97.21%, all indicating high true positive rates. The specificity for these classes also remained high, reinforcing the DT classifier's reliability in road surface condition classification. The K-nearest neighbors (KNN) classifier showed a lower accuracy of 95.35% compared to the other two models. Despite this, the KNN classifier still achieved commendable precision, recall, and f1-scores, with the macro average and weighted average scores falling between 94% and 95%. The sensitivity (recall) scores showed some variation, with Full of Holes Condition at 91.89%, Smooth Condition at 96.77%, and Uneven Condition at 93.80%, suggesting a slightly lower but still substantial ability to correctly classify the road conditions. The specificity values remained high, affirming the model's effectiveness. Finally, the ensemble model using a voting classifier achieved an accuracy of 97.26%. The predictions made by different classifiers were combined in this model, which had high precision, recall, and f1-scores across all classes, with weighted and macro averages close to 96% to 97%. The sensitivity (recall) for the Full of Holes Condition was at 95.20%, Smooth Condition at 98.22%, and Uneven Condition at 96.05%, indicating strong positive identification rates. The specificity values were also robust, indicating solid performance in differentiating between the conditions. In conclusion, all models demonstrated strong performance metrics, with the random forest classifier leading in almost all aspects, closely followed by the ensemble voting classifier. These models prove to be highly effective in classifying road surface conditions, with high sensitivity and specificity across the board. Figures 8 and 9 are horizontal bar charts that illustrate the feature contributions to the random forest model's predictions for two different samples, as seen via the tree interpreter tool. Show Table 3. In Figure 8, we see the feature contributions for Sample 21 which is predicted as smooth condition class. The chart displays a range of features along the \(y\)-axis, with their corresponding contribution to the model's prediction on the \(x\)-axis. Positive contributions towards the model's prediction are shown in blue, indicating that these features push the prediction towards the smooth condition class, whereas negative contributions are in red, suggesting an influence in the opposite direction. Sample 21 is the most substantial. Figure 8: Feature contributions for sample 21 for road surface classification.