  In this paper we discuss the techniques involved in the design of the famous
statistical spam filters that include Naive Bayes, Term Frequency-Inverse
Document Frequency, K-Nearest Neighbor, Support Vector Machine, and Bayes
Additive Regression Tree. We compare these techniques with each other in terms
of accuracy, recall, precision, etc. Further, we discuss the effectiveness and
limitations of statistical filters in filtering out various types of spam from
legitimate e-mails.
