## 3 Results
### Regression Experiment  
Table 7 shows the results for the regression experiments. For each technique, the MAE is presented as mean \(\pm\) SD (standard deviation) over the 20 test-set folds. In addition, the table includes the MAE for _fast_ learners (_short_ training sessions) and _slow_ learners (_long_ training sessions). The distinction was based on the total number of trials of the trainee (NTi) with respect to the median number of trials (mdNT): for _fast_ learners NTi \(<\)mdNT and for _slow_ learners NTi \(\geq\)mdNT (mdNT = 23, 23, and 16 for Task 5, 6, and 7, respectively). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **Evaluation** & \multirow{2}{*}{**kNN**} & **LDA** & **QDA** & **LR** & **GNB** & **RF** & \
\begin{tabular}{c} **SVM** \\ **(Linear)** \\ \end{tabular} &
\begin{tabular}{c} **SVM** \\ **(RBF)** \\ \end{tabular} \\ \hline \multirow{3}{*}{5} & Accuracy & 89.9 & **96.3** & 94.8 & 94.8 & 94.0 & 85.7 & 95.7 & 90.8 \\  & Specificity & 95.0 & **98.2** & 97.3 & 97.3 & 97.0 & 93.0 & 97.8 & 95.5 \\  & Sensitivity & 90.0 & **96.3** & 94.7 & 94.7 & 94.0 & 85.9 & 95.7 & 91.0 \\ \hline \multirow{3}{*}{6} & Accuracy & 90.9 & **95.9** & 91.7 & 91.7 & 94.4 & 86.5 & 94.4 & 91.7 \\  & Specificity & 95.3 & 82.7 & 96.0 & 96.0 & **97.3** & 93.2 & 97.2 & 96.2 \\  & Sensitivity & 90.7 & **96.0** & 92.0 & 92.0 & 94.6 & 86.4 & 94.3 & 92.0 \\ \hline \multirow{3}{*}{7} & Accuracy & 81.0 & **99.7** & 78.6 & 78.6 & 98.7 & 82.5 & 98.8 & 93.6 \\  & Specificity & 90.4 & **99.8** & 89.3 & 89.3 & 99.3 & 91.1 & 99.3 & 96.8 \\ \cline{1-1}  & Sensitivity & 80.7 & **99.7** & 78.6 & 78.6 & 98.7 & 82.1 & 98.7 & 93.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean performance comparison for 3-class classification (Tasks 5, 6, and 7). Best results row-wise are shown in bold. Figure 4: Color-coded confusion matrices (%) for the best performing algorithm for the 3-class classification (Tasks 5, 6, and 7); B: ‘Beginning of training’, M: ‘Middle of training’ and E: ‘End of training’. Regarding the results for _all_ training sessions, most of the ML algorithms show a MAE value close to 5.4-5.8 for Task 5, 4.3-4.9 for Task 6, and 3.6-4.1 for Task 7. SVR shows the best performance among the ML techniques with MAE = 5.4 (Task 5), 4.3 (Task 6), and 3.6 (Task 7). The naive approach, which is based only on historical data, seems to have the lowest performance for Task 5 (MAE = 7) and Task 6 (MAE = 5.5), whereas for Task 7, it is almost comparable to the ML algorithms (MAE = 4.1). The LSTM model presents the best performance against all other techniques with MAE = 4.0 (Task 5), 3.6 (Task 6), and 3.6 (Task 7). These values correspond to error percentages of 17.4% for Task 5, 15.7% for Task 6, and 22.5% for Task 7, which are calculated based on the median number of trials for each task as presented in Table 2.  
For _short_ training sessions, all ML algorithms exhibit an MAE similar to the MAE for _all_ training sessions (MAE = 5.2-6.3 for Task 5, MAE = 4.2-4.9 for Task 6, and MAE = 3.7-4.8 for Task 7). Again, SVR is the best performing ML algorithm with MAE = 5.2 (Task 5), 4.2 (Task 6), and 3.7 (Task 7). In contrast to _all_ training sessions, the naive approach shows the lowest performance for Task 7 (MAE = 5.0), while for Task 5 (MAE = 8.3) and 6 (MAE = 5.1), the performance is comparable to some ML algorithms. The LSTM model outperforms all other algorithms with MAE = 2.6 (Task 5), 3.6 (Task 6), and 3.6 (Task 7) corresponding to error percentages of 11.3% for Task 5, 15.7% for Task 6, and 22.5% for Task 7. For _long_ training sessions, most ML algorithms achieve MAE values close to 4.7-5.3 (Task 5), 3.8-4.6 (Task 6), and 3.2-4.0 (Task 7). The best performing algorithms for Tasks 5, 6, and 7 are LinR (MAE = 4.7; 20.4% error), LinR/SVR (MAE = 3.8; 16.5% error), and SVR/Naive (MAE = 3.2; 20.0% error), respectively. For Task 5, the LSTM model is slightly