Ten ways to fool the masses with machine learning
It highlights issues that hinder progress in ML by affecting reproducibility and validity, targeting researchers and reviewers to improve reporting practices.
The paper addresses the problem of misleading or irreproducible results in machine learning literature by humorously listing common pitfalls in reporting experiments, aiming to raise awareness among researchers.
If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that report results that are difficult to reconstruct or reproduce, results that mis-represent the performance of the system, or contain other biases that limit their validity. In this semi-humorous article, we discuss issues that arise in running and reporting results of machine learning experiments. The purpose of the article is to provide a list of watch out points for researchers to be aware of when developing machine learning models or writing and reviewing machine learning papers.