Table 9.
Performance of the machine learning classifiers after transfer learning.
| ML classifier models | Mean accuracy | Precision | Recall | F1-score | MCC |
|---|---|---|---|---|---|
| SVC (kernel = 'linear') | 84.70 | 85.00 | 84.00 | 84.00 | 80.29 |
| SVC (kernel = 'rbf') | 69.30 | 69.00 | 70.00 | 69.00 | 64.48 |
| GaussianNB () | 64.40 | 64.00 | 65.00 | 64.00 | 57.29 |
| DecisionTreeClassifier (criterion = "gini") | 97.51 | 97.00 | 98.00 | 97.00 | 96.73 |
| DecisionTreeClassifier (criterion = "entropy") | 97.99 | 98.00 | 98.00 | 98.00 | 96.79 |
| RandomForestClassifier (n_estimators = 50) | 96.90 | 96.00 | 96.00 | 96.00 | 96.08 |
| RandomForestClassifier (n_estimators = 100) | 97.10 | 97.00 | 97.00 | 97.00 | 96.41 |
| KNeighborsClassifier (n_neighbors = 2) | 81.35 | 81.00 | 82.00 | 81.00 | 77.79 |
| KNeighborsClassifier (n_neighbors = 4) | 83.25 | 81.00 | 82.00 | 81.00 | 79.78 |
| DummyClassifier | 27.31 | 27.00 | 27.00 | 27.00 | 00.00 |