Table 6 The results of the proposed model with different ML algorithms.
From: A hybrid super learner ensemble for phishing detection on mobile devices
Metrics | ML1 | ML2 | ML3 | ML4 | ML5 |
|---|---|---|---|---|---|
Recall (%) | 92.54 | 78.15 | 88.64 | 86.08 | 91.42 |
TNR (%) | 90.76 | 81.10 | 86.40 | 80.07 | 90.73 |
Precision (%) | 93.21 | 88.47 | 90.14 | 84.95 | 93.32 |
F1-Score (%) | 92.87 | 82.99 | 89.39 | 85.51 | 92.36 |
Accuracy (%) | 91.79 | 79.18 | 87.71 | 83.48 | 91.13 |
MCC (%) | 83.19 | 57.16 | 74.81 | 66.30 | 81.83 |
