[2108.02497] How to avoid machine learning pitfalls: a guide for academic researchers

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.

[2108.02497] How to avoid machine learning pitfalls: a guide for academic researchers --> Computer Science > Machine Learning arXiv:2108.02497 (cs) [Submitted on 5 Aug 2021 ( v1 ), last revised 29 Aug 2024 (this version, v5)] Title: How to avoid machine learning pitfalls: a guide for academic researchers Authors: Michael A. Lones View a PDF of the paper titled How to avoid machine learning pitfalls: a guide for academic researchers, by Michael A. Lones View PDF HTML (experimental) Abstract: Mistakes in machine learning practice are commonplace, and can result in a loss of confidence in the fin

saved by

• Amean Asad

related reading

• An Opinionated Guide to ML Researchjoschu.net
• Templates for machine learning research papers | Neel Guhaneelguha.github.io
• Just know stuff. (Or, how to achieve success in a machine learning PhD.) · Patrick Kidgerkidger.site
• Rules of Machine Learning: | Google for Developersdevelopers.google.com
• Questionable practices in machine learningarxiv.org
• Tim Rocktäschelrockt.github.io
• Why are Machine Learning Projects so Hard to Manage? | by Lukas Biewald | Mediummedium.com
• Tips for Empirical Alignment Research — AI Alignment Forumalignmentforum.org
• Preface - The Emerging Science of Machine Learning Benchmarksmlbenchmarks.org
• Sloppy Use of Machine Learning Is Causing a ‘Reproducibility Crisis’ in Science | WIREDwired.com
• How To Become A Mechanistic Interpretability Researcher — AI Alignment Forumalignmentforum.org
• MAI-Thinking-1: Building a Hill-Climbing Machinemicrosoft.ai