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Introducing the REFORMS checklist for ML-based science

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Summary

This article discusses the issue of data leakage in machine learning (ML)-based science and the need for clear reporting standards for researchers in order to avoid errors. The authors developed the REFORMS checklist, a 32-item checklist that can be used by researchers, reviewers, and journals to minimize errors in ML-based science. The checklist was developed by 19 researchers from different disciplines, and the paper and checklist can be found on their project website. The paper also provides a review of past failures and best practices for avoiding such failures.

Q&As

What is the purpose of the REFORMS checklist for ML-based science?
The purpose of the REFORMS checklist for ML-based science is to provide clear reporting standards for researchers to help improve the quality of research.

What is data leakage and how does it affect ML-based science?
Data leakage is when a model is evaluated on the same, or similar, data as it is trained on, which makes estimates of accuracy exaggerated. It can lead to errors in ML-based science.

What errors are common in ML-based science?
Errors of execution, such as data leakage, and errors of interpretation, such as spinning results or not clarifying the level of uncertainty in a model's output, are common in ML-based science.

What is the REFORMS checklist composed of?
The REFORMS checklist is composed of 32 items that can be helpful for researchers conducting ML-based science, referees reviewing it, and journals where it is submitted and published.

How can the REFORMS checklist help to minimize errors in ML-based science?
The REFORMS checklist can help to minimize errors in ML-based science by providing reporting standards for researchers, referees, and journals. It can also provide best practices for avoiding errors and help to make it more apparent when errors do creep in.

AI Comments

👍 This article is a great overview of the challenges with ML-based science. It provides clear and concise examples of the potential pitfalls and offers a useful checklist of standards to help improve the quality of research.

👎 This article fails to provide any real solutions to the issues of data leakage and misinterpretation, only offering a checklist of standards that are likely to be overlooked in practice.

AI Discussion

Me: It discusses the challenges of ML-based science and how a new checklist called REFORMS could help reduce errors. It also talks about how errors in ML-based science are common due to lack of clear reporting standards.

Friend: That's really interesting. It sounds like this checklist could be a major breakthrough for ML-based science.

Me: Yeah, it could certainly help reduce the number of errors in ML-based science. But there are still other issues that need to be addressed, like errors of interpretation and not properly defining the phenomenon being modeled. So while this checklist is a step in the right direction, there's still more work to be done.

Action items

Read the REFORMS pre-print and checklist to become familiar with the proposed standards for ML-based science.
Discuss the REFORMS checklist with colleagues and peers to gain insight into how it can be used to improve the quality of ML-based science.
Implement the REFORMS checklist in your own research to ensure that your results are accurate and reliable.

Technical terms

ML-based science: Machine learning-based science is a field of research that uses machine learning algorithms to analyze data and make predictions.
Data leakage: Data leakage is a common problem in machine learning where the model is evaluated on the same, or similar, data as it is trained on, which makes estimates of accuracy exaggerated.
REFORMS: REFORMS (Reporting standards for Machine Learning Based Science) is a checklist of 32 items that can be helpful for researchers conducting ML-based science, referees reviewing it, and journals where it is submitted and published.
Systematic review: A systematic review is a type of research that uses a systematic approach to identify, select, and critically appraise relevant research, and to collect and analyze data from the studies that are included in the review.
Preprint: A preprint is a version of a scientific paper that is made available online before it is published in a journal.