Table 4 Classification report with accuracy from experiment 1: Ensemble-based classifiers on the first dataset
From: Phishing website prediction using base and ensemble classifier techniques with cross-validation
Classifier Used | Confusion matrix | Kappa score (k) | Phishing (− 1) or non-phishing (1) | Precision | Recall | F1-score | Support | Accuracy achieved (%) |
|---|---|---|---|---|---|---|---|---|
Bagging Classifier | [[274 6] [ 2 210]] | 0.967 |  − 1 1 | 0.98 0.99 | 0.99 0.97 | 0.99 0.98 | 276 216 | 98.78 |
Adaboost classifier | [[272 8] [12 200]] | 0.917 |  − 1 1 | 0.97 0.94 | 0.96 0.96 | 0.96 0.95 | 284 208 | 95.91 |
Gradient boosting classifier | [[2728] [4 208]] | 0.950 |  − 1 1 | 0.97 0.98 | 0.99 0.96 | 0.98 0.97 | 276 216 | 97.56 |
Voting ensemble classifier | [[2728] [6 206]] | 0.942 |  − 1 1 | 0.97 0.97 | 0.98 0.96 | 0.97 0.97 | 278 214 | 97.15 |
Extra trees classifier | [[2728] [2 210]] | 0.959 |  − 1 1 | 0.97 0.99 | 0.99 0.96 | 0.98 0.98 | 274 218 | 99.18 |
XGBoost classifier | [[270 10] [11 201]] | 0.971 |  − 1 1 | 0.97 1.00 | 1.00 0.97 | 0.99 0.98 | 273 219 | 99.18 |