Comparison of Three Machine Learning Models for the Detection of Emails Spam

Abstract

Recently, machine learning has been applied into different major areas such as text classification, machine translation, and spam detection. The great performance of machine learning algorithms into several fields provided the humans with opportunities to tackle some of their hard jobs to be handled by machine learning systems. These tasks seem effortless for machines, and need less time as the amount of texts or spams need to be classified is huge. Hence, in his paper, we propose three different models for the task of emails spam detection. The three models are trained and validated on a public spam dataset. Experimentally, the models performed differently and it was seen that the Naïve Bayes outperformed the other machine learning algorithms in terms of accuracy and other evaluation metrics.