  The popularity, cost-effectiveness and ease of information exchange that
electronic mails offer to electronic device users has been plagued with the
rising number of unsolicited or spam emails. Driven by the need to protect
email users from this growing menace, research in spam email
filtering/detection systems has being increasingly active in the last decade.
However, the adaptive nature of spam emails has often rendered most of these
systems ineffective. While several spam detection models have been reported in
literature, the reported performance on an out of sample test data shows the
room for more improvement. Presented in this research is an improved spam
detection model based on Extreme Gradient Boosting (XGBoost) which to the best
of our knowledge has received little attention spam email detection problems.
Experimental results show that the proposed model outperforms earlier
approaches across a wide range of evaluation metrics. A thorough analysis of
the model results in comparison to the results of earlier works is also
presented.
