## 4 Results and Discussion
### Evaluation Results
Hyperparameter tuning involved an exhaustive grid search, where key parameters such as _cp_, _minbucket_, _splitrule_, _ntree_, and _mtry_ were adjusted to optimize the \(F1\)-_score_. This tuning process ensured that the models were fine-tuned to strike the right balance between sensitivity (_Recall_) and precision (_Precision_). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & _Precision_ & _Recall_ & _F1-score_ \\ \hline DecisionTrees/525 & 0.350 & 0.533 & 0.423 \\ DecisionTrees/7030 & 0.333 & 0.543 & 0.413 \\ DecisionTrees6040 & 0.283 & 0.648 & 0.394 \\ DecisionTrees5050 & 0.299 & 0.695 & 0.418 \\ RandomForests/525 & 0.442 & 0.455 & 0.448 \\ RandomForests/7030 & 0.415 & 0.509 & 0.457 \\ RandomForests6040 & 0.405 & 0.582 & 0.478 \\ RandomForests5050 & 0.392 & 0.591 & 0.471 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Models’ performance taking into account the _Precision_, _Recall_, and \(F1\)-_score_ metrics. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actual Positive (1)** & **Actual Negative (0)** \\ \hline Predicted Positive (1) & 64 & 94 \\ Predicted Negative (0) & 46 & 1002 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Confusion matrix for RandomForests6040, the best-performing model built. The analysis of the results clearly shows that the Random Forest model outperformed the Decision Tree model based on the \(F1\)-_score_ metric, which was the primary metric of interest. The Random Forest model proved to be more effective at capturing the complexities of the classification problem, resulting in more accurate predictions than the Decision Tree model. This is in line with previous studies, such as [PERSON] et al. [17], that demonstrate the superiority of Random Forest over other models such as the Decision Tree and Neural Network algorithms. Several steps were taken to refine the dataset, including normalization of continuous variables, encoding of categorical variables, and handling missing data. These preprocessing steps significantly improved the models' performance, particularly the Random Forest model, which is sensitive to feature scaling and encoding. Also, the superior performance of the Random Forest model can be attributed to its ensemble nature, which aggregates the predictions of multiple decision trees to reduce variance and improve generalization. Unlike the single Decision Tree, the Random Forest model was able to capture complex interactions between features, leading to higher accuracy and a better balance between _Precision_ and _Recall_. Identifying the most informative features, which are critical for solving a classification problem, is essential in addressing the underlying challenge. In this context, the concept of Impurity Decrease was used to evaluate each feature's contribution to reducing data impurity during the decision-making splits in the Decision Tree model. This approach was applied within the RandomForests6040 model, which demonstrated superior performance. The relative importance of features was assessed by calculating the average Impurity Decrease across all 103 trees in the model. The analysis identified \"Destination_Country\" as the most influential variable in the prediction task, followed closely by the \"Container_Height\", \"Container_Diameter\", and \"Temperature_Delivery_Date\" features. These findings are visually represented in Figure 5. The high importance of features like \"Destination_Country\" and \"Container_Height\" can be linked to the inherent logistics and handling challenges in different regions and the physical dimensions of the cargo, respectively. This insight is critical for logistics companies to understand where to focus their efforts in securing cargo during transit. Comparing the results of a study with the ones of the existing literature is crucial in research, as it helps validate and contextualize the findings within a broader knowledge framework.