Out-of-sample performance of the random forest (trained in an undersampling approach on spectra derived from three intracortical recordings in S1 of GAERS at a sensitivity of 90%) confronted to spectra derived from 24-h recordings in a separate group of GAERS (n = 9)
| Average confusion matrix | ||
|---|---|---|
| Predicted as false positive | Predicted as true positive | |
| False positive | 52.46 ± 9.38% | 47.54 ± 9.38% |
| True positive | 50.66 ± 8.95% | 49.34 ± 8.95% |
| Balanced accuracy | F1 score | |
| Rat 1 | 47.37% | 14.53% |
| Rat 2 | 53.68% | 6.89% |
| Rat 3 | 47.44% | 11.74% |
| Rat 4 | 49.07% | 4.82% |
| Rat 5 | 59.62% * | 9.14% |
| Rat 6 | 51.06% | 5.44% |
| Rat 7 | 51.93% | 7.18% |
| Rat 8 | 50.13% | 4.25% |
| Rat 9 | 47.82% | 9.68% |
Depicted in the upper panel is the average confusion matrix (±SEM), specifying the percentage of true positives correctly classified as true positives (lower right), true positives incorrectly classified as false positives (lower left), false positives correctly classified as false positives (upper left), and false positives incorrectly classified as true positives (upper right). Lower panel depicts the balanced accuracies and F1 scores for each individual rat. Note that the F1 score reflects the trade-off between false alarm rate/sensitivity. Low F1 scores are reflecting the drop of sensitivity associated to the drop of false alarm rate. As our goal in this work is the latter, the low scores are justified by the high balanced accuracies; * denotes an above chance balanced accuracy of classification as verified by surrogate statistics.