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The application of Decision Tree, KNN, and XGBoost for spam email detection has demonstrated to be greatly effective, with XGBoost standing out as the best robust model in terms of accuracy and AUC.
The simulation results emphasize the prospective for these algorithms to substantially improve spam detection processes by offering high precision and recall.
The broader impact of this work lies in enhancing cybersecurity, as it aids in the improvement of more trustworthy and efficient spam filters, defensive users from malicious content.
Additionally, his work underlines the importance of education in Ml, as it provides graduate students with practical insights into model evaluation, data cleaning and preprocessing, and real-world applications, fostering the development of serious problem-solving and practical skills
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IEEE/ICACT20250220 Slide.15
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