## 4 Results and Discussion
### The Predictive Performance
These comparisons rigorously assessed the statistical significance of observed performance differences. The null hypothesis for each test stated that the distributions of the compared model pairs were identical. All significance values were adjusted using the Bonferroni correction to account for multiple comparisons, with a significance level of 0.050. Under the GLHM sampling strategy, the pairwise comparisons for AUROC revealed a statistically significant difference only between NB and XGBoost (Adj. Significance = 0.044) *, indicating that XGBoost significantly outperformed Naive Bayes in terms of AUROC. No other model pairs (NB-SVM, NB-SVM-RF, SVM-SVM-RF, SVM-XGBoost, SVM-RF-XGBoost) showed statistically significant differences in AUROC, suggesting their performance distributions were comparable under this sampling approach. For Accuracy under GLHM sampling, a consistent pattern of statistically significant differences emerged. SVM-RF notably outperformed NB (Adj. Significance = 0.044), and similarly, SVM-RF also showed a significant difference when compared to XGBoost (Adj. Significance = 0.044). Furthermore, NB was significantly outperformed by XGBoost (Adj. Significance = 0.044) *. These results indicate clearer distinctions in accuracy performance among models when trained with GLHM sampling. Under the Random sampling strategy, the pairwise comparisons demonstrated more pronounced statistically significant differences. For AUROC, NB was significantly outperformed by SVM-RF (Adj. Significance = 0.044) * and even more strongly by XGBoost (Adj. Significance < 0.001 *). Similarly, SVM also showed highly significant differences compared to XGBoost (Adj. Significance < 0.001 *) and a significant difference compared to SVM-RF (Adj. Significance = 0.044) *. This suggests that XGBoost and SVM-RF, as well as SVM, were generally more distinct from their counterparts in AUROC under random sampling. For Accuracy under Random sampling, the differences were even more striking. NB was significantly outperformed by SVM (Adj. Significance < 0.001 *) and by XGBoost (Adj. Significance = 0.044) *. This highlights the substantial improvement these models offered over Naive Bayes in terms of accuracy when random sampling was employed. Other pairs, such as NB-SVM-RF, SVM-RF-SVM, SVM-RF-XGBoost, and SVM-XGBoost, did not show statistically significant differences in accuracy. These pairwise comparison results provide crucial statistical insights into the relative performance of the models and the impact of sampling strategies. While some models (e.g., SVM, SVM-RF, XGBoost) often performed numerically better, these tables confirm when such differences were statistically significant after Bonferroni correction. Notably, XGBoost consistently showed statistically significant superiority over Naive Bayes in both AUROC and Accuracy across both sampling strategies, underscoring its robust performance. The GLHM sampling strategy generally clarified differences in accuracy, while Random sampling revealed more profound distinctions in AUROC among model pairs. Under the Random sampling strategy, the analysis indicated that more complex models, specifically SVM-RF and XGBoost, generally demonstrated significantly better predictive performance and greater robustness compared to simpler models such as NB and SVM. This was evident, as NB was significantly outperformed by SVM-RF and XGBoost in AUROC, and by SVM and XGBoost in Accuracy. Similarly, SVM was significantly outperformed by SVM-RF and XGBoost in AUROC. This highlights the stronger capacity of SVM-RF and XGBoost to handle the higher levels of label noise and variability inherent in randomly sampled datasets, which disproportionately impacted the accuracy and stability of NB and SVM. Despite the noisier data, SVM-RF and XGBoost often exhibited statistically comparable predictive power among themselves under random sampling. Conversely, under the GLHM sampling strategy, the performance landscape shifted.