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The email spam detection performance of Decision Tree, KNN, and XGBoost has been evaluated based on multiple metrics such as accuracy, precision, recall, F1-score, and AUC. The results, as shown in the table, indicate that XGBoost outperforms the other models with the highest accuracy (96.78%), precision (94.20%), recall (94.20%), F1-score (94.20%), and AUC (99.51%). K-Nearest Neighbors also performs well, with an accuracy of 94.78%, high precision (97.04%), and a strong AUC (98.95%). Decision Tree, while effective, shows slightly lower performance with an accuracy of 91.95%, precision of 83.70%, and AUC of 90.78%. These results highlight the robustness of XGBoost for spam email classification, making it the most reliable model among the three evaluated.
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IEEE/ICACT20250220 Slide.14
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