## 3 Results
### Classification \"Driving Style\"
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Sensitivity** & **Model** & **Accuracy** \\ \hline Random Forest (RF) & 94.67\% & 99.05\% & 59.35\% & 0.97\% \\ \hline Decision Tree (DT) & 91.61\% & 95.05\% & 63.88\% & 0.95\% \\ \hline K-Nearest Neighbors (KNN) & 90.99\% & 96.24\% & 48.64\% & 0.95\% \\ \hline Voting Classifier & 93.89\% & 97.93\% & 61.34\% & 0.97\% \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of classification results for “driving style” attribute. Understanding that these contributions are specific to Sample 3 and not universally applicable to all predictions is essential. The relative influence of these features may vary significantly across different samples, reflecting the model's complex and non-linear decision-making process. For instance, a high 'Vehicle Speed Instantaneous' may be a strong predictor for an aggressive driving style in one context, but in another, it may be less indicative due to interactions with other features or different driving conditions. Moreover, the presence of red bars for 'Engine RPM' and 'Mass Air Flow' in this specific sample suggests a nuanced relationship between engine characteristics and driving style, highlighting the model's ability to capture the multifaceted nature of driving behavior. Interpreting these feature contributions allows us to gain insights into the model's reasoning, fosters transparency, and builds trust in the model's predictions. However, the model's interpretability also raises the challenge of understanding the conditional nature of these contributions. Analysts must carefully consider the operational domain and the potential for feature interactions, which can introduce complexity and uncertainty in the model's predictions. Figure 7 illustrates the impact of various features on the random forest model's prediction for Sample 20. Blue bars represent features that contribute positively to the prediction of the positive class, whereas red bars represent negative contributions that sway the prediction towards the opposite class. In this example, features such as 'Manifold Absolute Pressure' and 'Engine Load' have prominent blue bars, indicating that they are potent indicators that strengthen the model's prediction of the positive class. This could suggest that for Sample 20, certain conditions related to engine performance and load are more likely associated with the model's identified class, which, depending on the context, might relate to specific road or driving conditions. Figure 6: Feature contributions for sample 3 in driving style classification. On the other hand, significant red bars indicate that 'Engine Coolant Temperature' and 'Engine RPM' are predictive of the negative class in this specific case. It implies that in this sample, higher engine temperatures and RPMs are less indicative of the positive class and may instead be characteristic of alternative scenarios. The different feature contributions between Sample 3 and Sample 20 show that the model does not use a standard set of feature influences to make predictions. Instead, it changes the weight of each input based on the specifics of each sample. This adaptability underscores the complexity of the model's decision-making process and highlights the importance of considering individual sample characteristics when interpreting model predictions. Both figures are pivotal in understanding the random forest model's decision-making process for these predictions. They offer a granular view of the model's reasoning, enabling a deeper interpretation of the model's behavior and allowing us to trace the prediction back to its influential features. This level of detail is crucial for validating the model's performance, ensuring it aligns with domain knowledge, and establishing trust in the model's outcomes by making the AI's decision-making process more transparent.