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Table 7 Classification report with accuracy from experiment 2: Ensemble-based classifiers on the merged dataset with cross-validation

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

[[1211 44]

[37 1411]]

0.940

 − 1

1

0.96

0.97

0.97

0.97

0.97

0.97

1248

1455

98.51

Adaboost classifier

[[891 364]

[275 1173]]

0.523

 − 1

1

0.71

0.81

0.76

0.76

0.74

0.79

1166

1537

92.52

Gradient boosting classifier

[[1158 97]

[74 1374]]

0.873

 − 1

1

0.92

0.95

0.94

0.93

0.93

0.94

1232

1471

95.63

Voting ensemble classifier

[[1176 79]

[64 1384]]

0.894

 − 1

1

0.94

0.96

0.95

0.95

0.94

0.95

1240

1463

96.52

Extra trees classifier

[[1212 43]

[35 1413]]

0.942

 − 1

1

0.97

0.98

0.97

0.97

0.97

0.97

1247

1456

98.59

XGBoost classifier

[[1207 48]

[34 1414]]

0.939

 − 1

1

0.96

0.98

0.97

0.97

0.97

0.97

1241

1462

98.07