## 3 Results
### Classification Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **Evaluation** & \multirow{2}{*}{**kNN**} & \multirow{2}{*}{**LDA**} & \multirow{2}{*}{**QDA**} & \multirow{2}{*}{**LR**} & \multirow{2}{*}{**GNB**} & \multirow{2}{*}{**RF**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{1}{c}{**SVM**} \\  & & & & & & & & **(Linear)** & **(RBF)** \\ \hline \multirow{4}{*}{5} & Accuracy & 95.1 & 89.2 & 94.3 & 94.4 & 93.2 & 93.1 & **97.1** & 94.7 \\  & Specificity & 95.0 & **99.2** & 90.6 & 95.6 & 95.3 & 94.6 & 98.6 & 98.1 \\  & Sensitivity & 95.3 & 79.2 & **98.1** & 93.3 & 91.1 & 92.2 & 95.6 & 91.4 \\ \hline \multirow{4}{*}{6} & Accuracy & 92.6 & 94.6 & 95.1 & 95.4 & 92.5 & 95.6 & **96.9** & 95.0 \\  & Specificity & 91.4 & 97.8 & 95.0 & 97.2 & 92.5 & 95.8 & **98.9** & 96.4 \\  & Sensitivity & 93.9 & 91.4 & **95.3** & 93.6 & 92.5 & **95.3** & 95.0 & 93.6 \\ \hline \multirow{4}{*}{7} & Accuracy & 67.9 & **75.7** & 73.3 & 63.6 & 74.7 & 73.6 & 73.3 & 73.5 \\  & Specificity & 75.0 & **87.8** & 46.1 & 81.4 & 85.8 & 84.4 & 76.7 & 79.7 \\ \cline{1-1}  & Sensitivity & 60.8 & 63.6 & **81.1** & 68.1 & 60.8 & 62.8 & 70.0 & 67.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean performance comparison for 2-class classification based on PCA (Tasks 5, 6, and 7). Best row-wise results are in bold. Figure 3: Color-coded confusion matrices (%) for the best-performing algorithm for the 2-class classification (Tasks 5, 6, and 7). B and E denote the ‘Beginning of training’ and ‘End of training’, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **Evaluation** & \multirow{2}{*}{**kNN**} & \multirow{2}{*}{**LDA**} & \multirow{2}{*}{**QDA**} & \multirow{2}{*}{**LR**} & \multirow{2}{*}{**GNB**} & \multirow{2}{*}{**RF**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{1}{c}{**SVM**} \\  & & & & & & & **(Linear)** & **(RBF)** \\ \hline \multirow{4}{*}{5} & Accuracy & 90.7 & 91.4 & 83.2 & **93.6** & 91.9 & 92.6 & 92.8 & 92.8 \\  & Specificity & 89.7 & **95.3** & 70.8 & 91.7 & 93.1 & 93.6 & 91.1 & **95.3** \\  & Sensitivity & 91.7 & 87.5 & **95.6** & **95.6** & 90.8 & 91.7 & 94.4 & 90.3 \\ \hline \multirow{4}{*}{6} & Accuracy & 93.9 & 92.8 & 82.2 & 95.1 & 91.8 & 94.4 & 94.6 & **95.3** \\  & Specificity & 92.5 & 93.1 & 84.2 & 94.2 & 90.0 & 92.8 & 92.8 & **95.0** \\  & Sensitivity & 95.3 & 92.5 & 80.0 & **96.1** & 93.6 & **96.1** & 96.4 & 95.6 \\ \hline \multirow{4}{*}{7} & Accuracy & 63.3 & 70.8 & 52.9 & **72.1** & 55.0 & 68.5 & 71.9 & 67.9 \\  & Specificity & 66.7 & **82.2** & 8.9 & 79.4 & 19.4 & 73.6 & 81.1 & 80.3 \\ \cline{1-1}  & Sensitivity & 60.0 & 59.4 & **96.9** & 64.7 & 90.6 & 63.3 & 63.1 & 55.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean performance comparison for 2-class classification based on [PERSON]’s score (Tasks 5, 6, and 7). Best results row-wise are shown in bold. Across all three tasks, accuracy consistently exceeds 80%, with the top-performing algorithms achieving at least 96%. Once again, specificity outweighs sensitivity. Specifically, for Tasks 5, 6, and 7, the best-performing algorithm is LDA, achieving accuracies of 96.3%, 95.9%, and 99.7%, respectively. LDA also shows the highest sensitivity in all three tasks (96.3%, 96.0%, and 99.7%, respectively), and for Tasks 5 and 7, LDA achieves the highest specificity (98.2% and 99.8%, respectively). In Task 6, the highest specificity is presented by GNB (97.3%). Figure 4 presents the normalized confusion matrices for the best-performing algorithm for the 3-class classification experiment. Task 5 demonstrates superior classification performance for the Middle of training class, while both the Middle of training and End of training classes are perfectly classified in Task 7. In contrast, the Beginning of training class shows better classification performance in Task 6. The confusion matrices of all of the other algorithms can be found in Supplementary Materials, Figure S2.