## 4 Results and Discussion
### Evaluation Results
Evaluating the alignment or divergence of a new study's results with the ones of prior studies allows for an assessment of their consistency, reliability, and generalizability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(Split\) & _cp_ & \(Minbucket\) \\ \hline DecisionTrees7525 & Information Gain & 0.004 & 13 \\ DecisionTrees7030 & Information Gain & 0.004 & 10 \\ DecisionTrees6040 & Information Gain & 0.002 & 7 \\ DecisionTrees5050 & Information Gain & 0.001 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Optimal hyperparameters for the Decision Tree model to maximize \(F1\)-_score_. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(Splitrule\) & \(Ntree\) & \(Mtry\) & \(Nodesize\) \\ \hline RandomForests7525 & Information Gain & 111 & 2 & 7 \\ RandomForests7030 & Gini index & 106 & 2 & 16 \\ RandomForests6040 & Information Gain & 103 & 2 & 19 \\ RandomForests5050 & Gini index & 111 & 2 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Optimal hyperparameters for the Random Forest model to maximize \(F1\)-_score_. The findings of this study align with those of [PERSON] et al. (2017), as both identify the Random Forest model as the most effective approach for addressing the problem under study. However, a discrepancy arises in identifying the most influential feature for predicting complaints about shipping containers. While [PERSON] et al. (2017) identified the duration of container storage as the most informative variable, the current study found that the corresponding variable, \"Warehousing_Time\", did not show a statistically significant correlation with the target variable, \"Complaint (Y/N)\". This divergence in findings may be attributed to differences in the contexts of the two studies and the varying influence of business type, which could lead to different outcomes. The research by [PERSON] et al. (2015) offered valuable insights into predicting cargo loss severity during transportation. Their study highlighted Transit Types, Product Categories, and Shipping Destinations as key features. While the dataset used in the current study lacked sufficient information to directly verify the findings related to Transit Types and Product Categories, the identification of Shipping Destinations as a significant feature aligns with their results. Consistent with [PERSON] et al. (2015), the current study found \"Destination_Country\" to be the most critical factor in predicting pallet collapses during transport. This agreement on the importance of Shipping Destinations strengthens the validity of the current study's conclusions. This study makes a novel contribution to the existing literature by identifying new variables related to the geometry of the shipped product, specifically \"Container_Height\" and \"Container_Diameter\", as well as the average temperature recorded at the delivery location on the delivery date, denoted as \"Temperature_Delivery_Date\". These variables are found to be highly informative for predicting pallet collapse during transportation. This is the first study to recognize the significance of these variables in addressing the issue. Moreover, this research breaks new ground as the first to specifically focus on predicting pallet collapse during transportation. It also addresses the unique product-related challenges encountered in the glass industry, adding to its innovative approach.