Phishing Target Identification Based on Neural Networks Using Category Features and Images

Table 2

The performance of extracted features on seven machine learning classifiers.

Models	Accuracy	Macro-F1 score	Weighted-F1 score
SVM	0.3699	0.0459	0.2594
LR	0.4293	0.1343	0.3648
KNN	0.6318	0.3499	0.6042
DT	0.8394	0.6611	0.8319
RF	0.8586	0.7236	0.8429
XGBoost	0.8743	0.7075	0.8677
LightGBM	0.8848	0.7172	0.8769

The bold values represent the best values of the evaluation metrics.