## 3 Results
### Statistical Tests
#### 3.4.4. Corrected 10-Fold Cross-Validation Paired \(t\)-Test
\begin{table}
\begin{tabular}{c|c|c|c c c c c} \hline  & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline
**RF** & & C & aprfc & c & rC & APFC & PC \\ \hline
**ANN** & 1 & & aprfc & pRc & P & & \\ \hline
**LR** & **5** & **5** & & & APRFC & APRFC & APRFC & APRFC \\ \hline
**SVM** & 1 & **3** & **5** & & & rfC & APrC & PrC \\ \hline
**LGBM** & 2 & 1 & **5** & **3** & & & APFC & APC \\ \hline
**CB** & **4** & & **5** & **4** & **4** & & \\ \hline
**XGB** & **2** & & **5** & **3** & **3** & & \\ \hline \end{tabular}  
* A: Accuracy; P: Precision, R: Recall, F:F1-score, C: ROC-AUC. The colors represent the intensity of the statistically significant differences. Dark green indicates differenes identified across all performance metrics (regardless of direction), while no color indicates differences identified on one or no metrics. The yellow diagonal separates the lower triangle (number of significant differences) from the upper triangle (metrics where significant differences is identified).  
\end{table} 
Table 9: Aggregate performance table of Corrected 10-fold cross-validation paired \(t\)-test. statistically insignificant differences, with the exception of LGBM, where it exhibited a significant advantage on the F1 metric (t = 2.547, \(p\) = 0.011). CB emerged as one of the strongest classifiers across nearly all metrics, demonstrating significant advantages wherever statistical differences were identified, except against SVM in recall. It outperformed RF on four out of five metrics, LR on all metrics, and LGBM on all but recall, where the difference was statistically insignificant (t = 0.455, \(p\) = 0.649) but still in the same direction. Comparisons with XGB and ANN generally did not reach significance (e.g., XGB accuracy: t = 1.046, \(p\) = 0.296; ANN precision: t = 0.405, \(p\) = 0.685), suggesting that CB, XGB, and ANN occupy a similarly strong position among top-performing models. XGB likewise ranked among the best performers, particularly rivaling CB and ANN. It consistently outperformed LR in all five metrics (e.g., accuracy: t = 8.457, \(p\) < 0.001; precision: t = 5.121, \(p\) < 0.001, etc.). The comparisons with CB on accuracy and ROC-AUC showed no significant gap (t = \(-\)1.046, \(p\) = 0.296; t = \(-\)0.518, \(p\) = 0.604), suggesting an equivalently strong capacity. Similarly, with ANN model pairs, XGB did not find any statistical superiority. XGB held advantages over RF (precision: t = \(-\)2.797, \(p\) = 0.005; ROC-AUC: t = \(-\)4.552, \(p\) < 0.001), affirming its position as a consistent top-tier method across the examined metrics. Also, it statistically outruns three out of five metrics in comparison with LGBM. LR consistently lagged behind its counterparts across accuracy, precision, recall, f1, and ROC-AUC. It was decisively outperformed by RF (accuracy: t = \(-\)6.967, \(p\) < 0.001; precision: t = \(-\)2787, \(p\) = 0.005) and ANN (accuracy: t = \(-\)7.090, \(p\) < 0001; precision: t = \(-\)4338, \(p\) < 0.001). Comparisons against CB, ANN, and XGB likewise revealed statistically significant disadvantages, with \(p\)-values below 0.001 in most pairwise tests. These patterns indicate that LR occupied the lowest tier among the evaluated models.