## 3 Results
### Statistical Tests
#### 3.4.2 5 \(\times\) 2 Cross-Validation Paired \(t\)-Test  
A pairwise 5 \(\times\) 2 cross-validation paired \(t\)-test was conducted to evaluate all model pairs included in this research, and the resulting outcomes are presented in Table 7, which is divided into two parts. The lower triangular section of Table 7, situated below the main diagonal, provides the total count of detected statistically significant differences (\(p\) < 0.05) across the five examined performance metrics (accuracy, precision, recall, F1-score, and ROC-AUC) for each model pair. For instance, the cell corresponding to the Random Forest (RF) and Logistic Regression (LR) comparison contains the value 5, indicating that statistically significant performance gaps (\(p\) < 0.05) were identified for all five metrics under the 5 \(\times\) 2 paired \(t\)-test. This figure can range from a maximum of 5 to a minimum of 0, with zeros omitted from the table to ensure readability. The upper triangular region of Table 7, located above the main diagonal, displays letters corresponding to the metrics for which a statistically significant difference emerged for a given pair of models: A for Accuracy, P for Precision, R for Recall, F for F1-score, and C for ROC-AUC. Uppercase letters (A, P, R, F, C) denote instances where the column-based model (model1) significantly outperforms the row-based counterpart (model2), while lowercase letters (a, p, r, f, c) indicate the opposite, where model2 outperforms model1. In the previously mentioned RF/LR example, the upper cell lists all five lowercase letters, implying that the LR model significantly underperformed the RF model on every measured metric. This nomenclature remains consistent across further statistical tests and is therefore not elaborated again. Complete statistical test results for all pairs/models, including exact t-scores and p-values, are available in Appendix A Table A1. The conducted 5 \(\times\) 2 CV paired \(t\)-test revealed that all comparisons involving Logistic Regression (LR) produced statistically significant differences (\(p\) < 0.05). This outcome corroborates the findings in both Figure 3 and Table 5, which indicate that Logistic Regression yields considerably poorer classification performance relative to the other methods. The statistical test has now confirmed that the visibly weaker performance of LR, observed in earlier tables and figures, is robust to formal hypothesis testing. Among other model comparisons, Support Vector Machine (SVM) was found to differ significantly from several \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline
**RF** & — & \(1.655/(0.198)\) & **47.696/(0.000)** & \(0.735/(0.391)\) & \(0.089/(0.766)\) & **4.628/(0.032)** & \(1.655/(0.198)\) \\ \hline
**ANN** & \(1.655/(0.198)\) & — & **50.080/(0.000)** & \(0.391/(0.532)\) & \(3.309/(0.069)\) & \(0.632/(0.427)\) & \(0.015/(0.904)\) \\ \hline
**LR** & **47.696/(0.000)** & **50.08/(0.000)** & — & **48.497/(0.000)** & **40.688/(0.000)** & **54.280/(0.000)** & **48.525/(0.000)** \\ \hline
**SVM** & \(0.735/(0.391)\) & \(0.391/(0.532)\) & **48.497/(0.000)** & — & \(1.688/(0.194)\) & \(2.717/(0.099)\) & \(0.431/(0.511)\) \\ \hline
**LGBM** & \(0.089/(0.766)\) & \(3.309/(0.069)\) & **40.688/(0.000)** & \(1.688/(0.194)\) & — & **8.491/(0.004)** & \(3.516/(0.061)\) \\ \hline
**CB** & **4.628/(0.032)** & \(0.632/(0.427)\) & **54.280/(0.000)** & \(2.717/(0.099)\) & **8.491/(0.004)** & — & \(\mathrm{nan}/(0.210)\) \\ \hline
**XGB** & \(1.655/(0.198)\) & \(0.015/(0.904)\) & **48.525/(0.000)** & \(0.431/(0.511)\) & \(3.516/(0.061)\) & \(\mathrm{nan}/(0.210)\) & — \\ \hline \end{tabular} Bold values indicate statistically significant differences based on [PERSON]’s test (\(\chi^{2}\), \(p\)-value in parentheses).  
\end{table} 
Table 6: [PERSON]’s Test Analysis of Classifier Performance: Chi-Square Statistics and Statistical Significance (\(p\)-Values).