Research Article
PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks
Table 7
Results of the 10-fold cross-validation.
| Fold | Accuracy (%) | Precision (%) | Recall (%) | F-measure (%) | AUC (%) |
| 1 | 95.86 | 97.15 | 94.49 | 95.8 | 99.09 | 2 | 95.93 | 97.41 | 94.38 | 95.87 | 99.04 | 3 | 95.69 | 96.53 | 94.78 | 95.65 | 98.99 | 4 | 96.01 | 97.26 | 94.7 | 95.96 | 99.07 | 5 | 95.62 | 97.36 | 93.78 | 95.54 | 98.99 | 6 | 95.81 | 97.03 | 94.52 | 95.76 | 98.99 | 7 | 95.86 | 97.12 | 94.54 | 95.81 | 99.06 | 8 | 95.64 | 97.48 | 93.7 | 95.55 | 98.98 | 9 | 95.83 | 98.07 | 93.51 | 95.74 | 99.1 | 10 | 95.69 | 97.29 | 94.01 | 95.62 | 98.97 | Average | 95.79 | 97.27 | 94.24 | 95.73 | 99.03 |
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