Hyperparameter Optimization of Ensemble Models for Spam Email Detection

Unsolicited emails, popularly referred to as spam, have remained one of the biggest threats cybersecurity globally. More than half emails sent in 2021 were resulting huge financial losses. The tenacity and perpetual presence adversary, spammer, has necessitated need for improved efforts at filtering spam. This study, therefore, developed baseline models random forest extreme gradient boost (XGBoost) ensemble algorithms detection classification spam using Enron1 dataset. then optimized grid-search cross-validation technique search hyperparameter space optimal values. performance (un-tuned) tuned both evaluated compared. impact tuning on was also examined. findings experimental study revealed that when compared with models. RF XGBoost achieved an accuracy 97.78% 98.09%, a sensitivity 98.44% 98.84%, F1 score 97.85% 98.16%, respectively. model outperformed model. is effective efficient email detection.