Table 3.
The table reports the accuracy, recall, and precision measures for each ML model. Results are reported for the 10-fold cross-validation (training) set and the test set, for both the time pressure and no time pressure groups.
| Training set (10-fold cross-validation) |
Test set | |||||
|---|---|---|---|---|---|---|
| Accuracy | Recall | Precision | Accuracy | Recall | Precision | |
| No time pressure models | ||||||
| Logistic | 100% | 1.00 | 1.00 | 85% | 0.85 | 0.854 |
| SVM | 98.53% | 0.985 | 0.986 | 75% | 0.750 | 0.753 |
| Naive Bayes | 100% | 1.00 | 1.00 | 75% | 0.750 | 0.753 |
| Random forest | 98.53% | 0.985 | 0.986 | 75% | 0.750 | 0.753 |
| LMT | 97.06% | 0.971 | 0.972 | 75% | 0.750 | 0.775 |
| Time pressure models | ||||||
| Logistic | 98.51% | 1.00 | 0.986 | 95% | 0.95 | 0.955 |
| SVM | 98.51% | 0.985 | 0.986 | 95% | 0.95 | 0.955 |
| Naive Bayes | 100% | 1.00 | 1.00 | 95% | 0.95 | 0.955 |
| Random forest | 97.01% | 0.970 | 0.970 | 95% | 0.95 | 0.955 |
| LMT | 95.52% | 0.955 | 0.959 | 95% | 0.95 | 0.955 |