| # | method | algorithm | train | Test 1: group 1Precision, recall, f1, accuracy | Test 2: group 2 | Test 3: group 3 |
| --- | --- | --- | --- | --- | --- | --- |
| 1.a.i | Machine learning (2 methods) | SVM | Train 1 or 2 or 3 / [respective own train set] | Precision: 0.6301 Recall: 0.6684 F1-Score: 0.5532 Accuracy: 0.6684 | Precision: 0.5402 Recall: 0.6099 F1-Score: 0.4935 Accuracy: 0.6099 | Precision: 0.4200 Recall: 0.6481 F1-Score: 0.5097 Accuracy: 0.6481 |
| 1.a.ii |  | SVM | TRAIN | Precision: 0.6119 Recall: 0.6782 F1-Score: 0.6182 Accuracy: 0.6782 | Precision: 0.5699 Recall: 0.6222 F1-Score: 0.5624 Accuracy: 0.6222 | |
| 1 b.i |  | K-Nearest Neighbors (KNN) | Train 1 or 2 or 3 / [respective own train] | Precision: 0.5941 Recall: 0.6684 F1-Score: 0.5544 Accuracy: 0.6684 | Precision: 0.4766 Recall: 0.5964 F1-Score: 0.4870 Accuracy: 0.5964 | Precision: 0.5098 Recall: 0.6567 F1-Score: 0.5366 Accuracy: 0.6567 |
| 1.b.ii |  | KNN | TRAIN | Precision: 0.5066 Recall: 0.6398 F1-Score: 0.5233 Accuracy: 0.6398 | Precision: 0.5641 Recall: 0.6117 F1-Score: 0.5479 Accuracy: 0.6117 |  |