# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Overall Results--Discussion  
Overall, the accuracies of GB, RF and LR classifiers increased for the \"traffic status\" dataset. Generally, despite the fact that some differences may exist, the two sets of data describe a set of features that are connected to each other. The \"lane detection\" dataset focuses more on the features of the targeted car relative to the road. On the other hand, the \"traffic status\" dataset describes features relating the target (car of interest) with its surrounding vehicles/objects. As a result, the second dataset provides a rich and diverse set of data to determine the state of mind of the driver. In conclusion, although the obtained results are not the same, the models built based on these datasets gave consistent results, particularly the gradient boosting algorithm.  
Despite the use of imbalanced samples for each class (aggressive, normal and drowsy), the gradient boosting algorithm provides consistent and encouraging results with both datasets across all classes. This helps in further substantiating the robustness of the model with various types of data set features. A level of consistency is observed.  
The authors in [47] employed all features in one single model with the entire data. This also provides a probabilistic result with the possibility for the driver to be normal, drowsy or aggressive. On the other hand, two feature sets and their corresponding models were run for robustness. The results obtained in this paper provide a clear classification for each set of features. The driver is classified as being in only one out of normal, drowsy or aggressive states. In terms of classification metrics, the accuracies of both models are comparable.  
Further research can be built on the improvement of the datasets. Even though the models were run separately on the datasets, consistent results were provided by these models. It should be noted that the two datasets use features that have a high correlation with the target classes. Combining these two datasets and other, more similar datasets could help in providing more concrete classification results. Moreover, the results from various datasets could be used to obtain a weighted percentage of the likelihood of the prediction being true. A concrete value could then be allotted to the driver being in an aggressive, drowsy or normal state of mind.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Logistic Regression** & **Gradient Boosting** & **Random Forests** & **Neural Networks** \\ \hline
**Accuracy** & 0.54 & 0.67 & 0.63 & 0.29 \\
**Micro Precision** & 0.54 & 0.67 & 0.63 & 0.29 \\
**Micro Recall** & 0.54 & 0.67 & 0.63 & 0.26 \\
**Micro F1-score** & 0.54 & 0.67 & 0.63 & 0.25 \\
**Macro Precision** & 0.53 & 0.70 & 0.68 & 0.29 \\
**Macro Recall** & 0.54 & 0.67 & 0.63 & 0.27 \\
**Macro F1-score** & 0.53 & 0.68 & 0.63 & 0.25 \\
**Weighted Precision** & 0.53 & 0.69 & 0.67 & 0.29 \\
**Weighted Recall** & 0.54 & 0.67 & 0.63 & 0.28 \\
**Weighted F1-score** & 0.53 & 0.67 & 0.63 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Traffic status—results comparison.  
Furthermore, the model results were obtained from motorways road data. Adding other diverse types of roads, such as secondary roads and their corresponding features, could be used to set up a large database. This would result in a better account of the robustness of the model on various roads, including other factors.