This document is a concise outline of 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.

翻译：本文简要概述了使用机器学习时常见的错误及其规避方法。尽管本文适用于具备机器学习基础知识的所有读者，但其最初面向研究学生，重点关注学术研究中特别需要谨慎的问题，例如严格进行比较并得出有效结论的需求。本文涵盖机器学习过程的五个阶段：模型构建前的准备工作、如何可靠地构建模型、如何稳健地评估模型、如何公平地比较模型，以及如何报告结果。