  This document outlines some of the common mistakes that occur when using
machine learning, and what can be done to avoid them. Whilst it should be
accessible to anyone with a basic understanding of machine learning techniques,
it was originally written for research students, and focuses on issues that are
of particular concern within academic research, such as the need to do rigorous
comparisons and reach valid conclusions. It covers five stages of the machine
learning process: what to do before model building, how to reliably build
models, how to robustly evaluate models, how to compare models fairly, and how
to report results.
