# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 5 Results and Discussion
### Comparison Results  
Figures 6 and 7 show the results for the default and optimised model settings. The results report the performance of the observed classifiers based on different missing data methods. The following can be observed: the results obtained using default Figure 6a settings for the six classifiers did not perform well across the different missing data methods applied to the RTA dataset. However, RF (97%) performed much better in terms of all the model evaluations. These results could be due to RF offering efficient test error estimates without experiencing any cost and offering reliable feature importance approximation. The AdaBoost classifier showed the lowest performance across all evaluation methods. AdaBoost could have performed poorly because it cannot handle data with outliers well. Figure 6b shows results obtained for RMSE where the RF model obtained the lowest value of 0.01, which means the model had lower errors when compared to the other methods. In terms of precision, the RF model achieved the best value of 93% when the mean and kNN missing value methods data was used in Figure 6c. Then results presented in Figure 6d for recall show that RF model obtained a high value of 89% when the mean missing value data method was utilised. In addition, MICE performed well across the used classifiers compared with the mean, k-NN, and median missing value methods. Concerning the Figure 7 model optimisation results, the following can be observed: Figure 7a RF performed slightly better overall than the other classifiers across all the evaluation methods with an accuracy of 97%. One possible reason is that RF can handle thousands of inputs without deleting any variables. The RF settings were tuned to entropy and n_estimators = 10. The RF results show that parameter tuning did not improve the results, as shown in Figure 7a. Furthermore, LR and SVC performed poorly when compared with the other classifiers. In terms of RMSE in Figure 7b, RF model obtained 0.12, which mean the model has low errors compared to the others. Figure 7c shows precision results, which revealed that the RF performed well by obtaining 93% when mean missing value data was considered. Finally, Figure 7d shows that RF achieved the best value of 89% compared with the other methods such as SVC, NB, kNN, LR and AdaBoost. In general,RF obtained promising results when compared with the other classifiers. The following graphs present results for PCA and LDA. Figure 6: Default settings performance results. Figure 7: Model optimisation performance results. Figures 8 and 9 contain graphical results computed using the RTA dataset. They include PCA and LDA dimensionality reduction techniques, which means reducing a large number of features to a smaller number of PCs. The results report the performance of the six classifiers based on four different missing data methods. The following is observed from the results: in the PCA results in Figure 8a, the RF classifier performed reliably and much better with accuracy (93%), precision and recall performance metrics when using MICE data. The MICE imputation method performed better compared with the other missing value methods because, as pointed out in Section 4, it captured 67% of the overall dataset. With a high RMSE, the AdaBoost classifier is the most poorly performing classifier. Figure 8b shows the results of the RMSE for all the augmented datasets and the results revealed that RF obtained a very low RMSE value of 0.27 compared to the other methods. Figure 8c show that the RF models achieved the best results in terms of precision, with MICE imputation methods achieving 93%. Then in Figure 8d, the graph presents recall results, which revealed that RF in terms of mean and median obtained 84%. The LDA results in Figure 9a indicate that the RF (94%) and k-NN (94%) classifiers performed comparatively better than the other classifiers and the PCA results. AdaBoost (82%) was the classifier with the lowest performance results across all the evaluation metrics.