| 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 |
|
