## 4 Results  
Utilizing 70% of the crash data for training, 15% for validation, and the remaining 15% for testing, two decision tree models employing the CART and RUMC-CART techniques were developed. These models were developed to predict crash severity levels, distinguishing between \"slight injury\" and \"serious injury\", with the latter including crashes with serious or fatal crashes, as mentioned earlier. The Gini impurity function guided the growth of a total of 60 nodes in the CART model and 56 nodes in the RUMC-CART model. Evaluation metrics for both the training and testing data, including classification results and the area under the ROC curve, are detailed in Table 3 and Figure 5.  
As shown in Table 3, CART exhibits robust accuracy across the training, validation, and testing datasets, with values of 0.79, 0.78, and 0.79, respectively. However, the model faces challenges in capturing positive instances, as evidenced by low recall values ranging from 0.04 to 0.06. The specificity remains consistently high at 0.99, indicating a strong ability to correctly identify negative instances. In terms of precision, CART demonstrates moderate values, decreasing from 0.58 in training to 0.52 in testing. The \(F1\) score, which indicates the balance between precision and recall, shows a range from 0.07 to 0.11. Despite facing these challenges, CART maintains an AUC value of 0.66 across all datasets, indicating its distinguishing abilities. Focusing on the RUMC-CART model, its accuracy is comparatively lower, with values of 0.63 for the training set, 0.61 for the validation set, and 0.62 for the testing set. The model performs well in identifying cases, as indicated by recall values ranging from 0.58 to 0.59. This highlights the model's strength in recognizing instances of the class, especially in scenarios where capturing positive cases accurately is crucial. The specificity values for RUMC-CART range from 0.65 to 0.68, suggesting challenges in identifying negative instances. The precision values are stable between 0.63 and 0.64, showcasing the consistent accuracy of the RUMC-CART in positive predictions. The \(F1\) score for RUMC-CART varies from 0.60 to 0.61, showing its balance between precision and recall. The ROC curves in Figure 5 provide a visualization of the performance of both the CART and RUMC-CART models across various sets, demonstrating their robust performance on different datasets. Table 4 presents the confusion matrices for the decision trees concerning the training, validation, and testing datasets. In these matrices, 0 represents \"slight injury\" crashes and 1 indicates \"serious injury\" crashes. Notably, the CART model exhibits higher precision in predicting the \"slight injury\" class compared to the \"serious injury\" class. In the training  
Figure 5: ROC curves of the training, validation, and testing datasets. dataset, 28,205 instances (99%) of the \"slight injury\" class and 470 instances (6%) of the \"serious injury\" class were accurately predicted. Similarly, the validation and testing datasets yielded precise predictions for the \"slight injury\" class, with 6151 instances (99%) and 6219 instances (99%) correctly classified, and for the \"serious injury\" class, with 369 instances (18%) and 90 instances (5%) correctly predicted, respectively. The CART model also presented challenges, misclassifying 339 instances (1%) of \"slight injury\" crashes in the training dataset as \"serious injury\" crashes. Furthermore, a substantial number of serious injury crashes, amounting to 7462 instances (94%), were inaccurately classified as \"slight injury\" crashes. The false positive (_FP_) and false negative (_FN_) values manifested as 86 instances (1%) and 82 instances (1%) of \"slight injury\" crashes being misclassified as \"serious injury\" crashes and 1639 instances (82%) and 1554 instances (95%) of \"serious injury\" crashes being erroneously predicted as \"slight injury\" crashes in the validation and testing datasets, respectively.