## 3 Results
A learning curve typically plots a performance metric; in this work, we considered the accuracy on the y-axis against the number of training examples or the training set size on the x-axis. Accuracy is computed on both training and test set. The learning curve shows how the model's performance evolves as more data points are included in the training process. The learning curve can exhibit different patterns that convey valuable information about the model:  
* Overfitting: If the learning curve shows high performance on the training set but significantly lower performance on the validation set, with a large gap between the two curves. This indicates that the model is capturing noise or specific patterns in the training data that do not generalize well to unseen data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **AUC** \\ \hline Ada Boost & **43.33\%** & **60.48\%** \\ Decision Tree & 38.63\% & 53.89\% \\ Random Forest & 34.00\% & 56.91\% \\ Light Gradient Boosting Machine & 36.00\% & 53.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy and AUC of the considered models for test battery B1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **AUC** \\ \hline Ada Boost & 43.27\% & 59.81\% \\ Decision Tree & 39.88\% & 55.70\% \\ Random Forest & **45.03\%** & 62.79\% \\ Light Gradient Boosting Machine & 44.46\% & **63.39\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy and AUC of the considered models for test battery B2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **AUC** \\ \hline Decision Tree & 73.29\% & 80.48\% \\ Random Forest & 83.08\% & 94.53\% \\ Light Gradient Boosting Machine & 83.98\% & 96.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy and AUC of the considered models for test battery [B3]. * Underfitting: If the learning curve demonstrates low performance on both the training and validation sets. In this case, more data are needed or the model is probably too simple and is unable to capture the underlying patterns in the data. * Convergence: Ideally, a learning curve shows that the model's performance on both the training and validation sets converge or stabilize as more data are included. This suggests that the model is learning from the data and generalizing well to unseen examples. The results in Figure 7a suggest that the model leads to a poor generalization, with probable model overfitting on the training dataset, while Figure 7b shows a better ability to generalize the model on the data. To better investigate the performance of the LBGM model and [B3] test battery, in Figure 8 the OvR ROC curves and the micro and macro average ROC curves for each class are compared. In Figure 9, precision-recall curve is displayed for LBGM, illustrating a favorable balance between false positive and false negative rates. Furthermore, Figure 10 provides additional insights into which features have the most influence on the model's predictions, allowing researchers and practitioners to understand the relative importance of different variables in the model's decision-making process. Significant features have been selected through PyCaret's feature importance permutation technique. This method involves randomly permuting the values of a single feature and measuring the impact on the model's performance. By comparing the model's performance before and after permuting the feature, the importance of the feature can be determined. Features with larger performance drops after permutation are considered more important. The analysis reveals that the cumulative power in the theta band at the Pz electrode and the gamma band are particularly influential in predicting the target variable. These findings are consistent with prior research in the field, which has consistently identified the theta and gamma frequency bands as relevant indicators in similar contexts.