## 3 Results
### Statistical Tests
#### 3.4.3 10-Fold Cross-Validation Paired \(t\)-Test  
The results of the pairwise 10-fold cross-validated paired \(t\)-test are presented in Table 8, which summarizes the number of statistically significant differences across all models and all pairs evaluated in this study. Full and detailed 10-folc-CV results are shown in Appendix A Table 10.  
A substantially higher number of performance differences was observed compared to the 5 \(\times\) 2 cross-validation test, with statistically significant differences detected across nearly all model pairs--where at least one metric exhibited significance. An exception was \begin{table}
\begin{tabular}{c|c|c|c c c c} \hline \hline  & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline
**RF** & & aprfc & c & & & \\ \hline
**ANN** & & & arfc & c & & \\ \hline
**LR** & 5 & 4 & & APRF & AFC & APFC & APFC \\ \hline
**SVM** & 1 & 1 & 4 & & C & C & C \\ \hline
**LGBM** & & & 3 & 1 & & & \\ \hline
**CB** & & & 4 & 1 & & & \\ \hline
**XGB** & & & 4 & 1 & & & \\ \hline \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 differences 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 7: Aggregate pairwise performance of 5 \(\times\) 2 CV paired \(t\)-test. noted for the XGBoost/CatBoost (XGB/CB) pairing, where no differences were identified across any metrics. The disparities were predominantly concentrated in pairs involving the Logistic Regression (LR) model, which demonstrated systemic deficiencies in performance relative to other algorithms. However, the 10-fold CV paired \(t\)-test also highlighted discrepancies in pairs containing CatBoost (CB), with CB exhibiting discriminative superiority over multiple counterparts, whereas LR consistently ranked as the lowest-performing model (Table 5). Under the accuracy metric, CB exhibited a consistently superior performance, shown by its significantly better outcomes against most of the competing models. Although XGB approached CB with no conclusive difference established between them (t = 1.707 \(p\) = 0.122), suggesting comparable accuracy levels. Logistic Regression, on the other hand, ranked consistently lower (with a t-score around and above 5) than the other methods and showed significant inferiority in the vast majority of pairwise comparisons. Other models demonstrated intermediate performance, frequently producing accuracy scores that were neither statistically distinguishable from the leading methods nor markedly superior or definitively inferior. With respect to precision, three algorithms--ANN, CB, and XGB--emerged as top performers. Their pairwise comparisons did not yield statistically significant disparities, indicating that they occupied a similar performance tier. Each of these three methods, however, significantly surpassed RF, LR, SVM, and LGBM in multiple tests. The recall results differ from the patterns observed in accuracy and precision. The SVM proved to be the most outstanding model for this metric, recording a significantly higher recall than most of its counterparts. RF also demonstrated moderately high recall (RF vs. ANN, t = 2.949, \(p\) = 0.016, RF vs. LR, t = 14.933, \(p\) < 0.001), and it did not differ significantly from certain strong competitors like CB, XGB, SVM, and LGBM. Logistic Regression performed poorly once more, losing decisively against the majority of comparisons. CB achieved prominent F1 scores, significantly surpassing many competing classifiers, including RF, ANN, LR, and LGMB. However, the differences between CB and certain other methods were statistically insignificant, suggesting that SVM and XGB perform at a comparable level for F1.