Figure 4 - data

<b>Figure 4. Comparison between best performing ML models. a)</b> F1 against threshold from the 10-best models of each architecture as evaluated in the test set, and the 10-best filters of all possible parametric combinations. Each line represents the performance of one trained model, colored by its maximal F1 (mean from all sessions is plotted in dark color). <b>b)</b> F1-scores for the best model of panel A, and ensemble model of panel e). Thresholds used are: 0.4 for XGBoost, 0.5 for SVM, 0.4 for LSTM, 0.1 for CNN2D, 0.5 for CNN1D, 4.5SD for Filter. <b>c)</b> Stability index for the same models as in panel b) (bottom), and the stability index vs the F1 (top). <b>d)</b> Similarity between predicted events of different architectures. Models are the same as in panels b)-c). To measure the similarity, the mean F1 across test sessions have been computed, using detected events in the y-axis as detections, and detected events in the x-axis as ground truth. Note the similarity between LSTM and 1D-CNN (white *), and that by XGBoost against SVM, LSTM and 1D-CNN (white +).<b> e)</b> Ensemble model, trained using the output of the best models of the machine learning architectures. Weights were: w1=-0.11 (XGBOOST); w2=-1.56 (SVM); w3=5.33 (LSTM); w4= 2.03 (2D-CNN); w5= 4.07 (1D-CNN); bias= -4.97. On the right, mean F1 score (line) ±95% confidence interval (shadow) for test sessions. Maximum F1-score and stability index for test sessions has been included in panels b) and c) to facilitate comparison with the rest of the methods.