Leakage and the reproducibility crisis in machine-learning-based science

• Published in: Patterns (New York, N.Y.) N.Y.), 2023-09, Vol.4 (9), p.100804, Article 100804
• Main Authors: Kapoor, Sayash, Narayanan, Arvind
• Format: Article
• Language: English
• Subjects: leakage; machine learning; reproducibility
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2023.100804

Machine-learning (ML) methods have gained prominence in the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. We systematically investigate reproducibility issues in ML-based science. Through a survey of literature in fields that have adopted ML methods, we find 17 fields where leakage has been found, collectively affecting 294 papers and, in some cases, leading to wildly overoptimistic conclusions. Based on our survey, we introduce a detailed taxonomy of eight types of leakage, ranging from textbook errors to open research problems. We propose that researchers test for each type of leakage by filling out model info sheets, which we introduce. Finally, we conduct a reproducibility study of civil war prediction, where complex ML models are believed to vastly outperform traditional statistical models such as logistic regression (LR). When the errors are corrected, complex ML models do not perform substantively better than decades-old LR models. •Data leakage is a flaw in machine learning that leads to overoptimistic results•Our survey of prior reviews shows leakage affects 294 papers across 17 scientific fields•We provide a taxonomy of leakage and introduce model info sheets to mitigate it•We show how leakage can lead to overoptimism with a case study on civil war prediction Machine learning (ML) is widely used across dozens of scientific fields. However, a common issue called “data leakage” can lead to errors in data analysis. We surveyed a variety of research that uses ML and found that data leakage affects at least 294 studies across 17 fields, leading to overoptimistic findings. We classified these errors into eight different types. We propose a solution: model info sheets that can be used to identify and prevent each of these eight types of leakage. We also tested the reproducibility of ML in a specific field: predicting civil wars, where complex ML models were thought to outperform traditional statistical models. Interestingly, when we corrected for data leakage, the supposed superiority of ML models disappeared: they did not perform any better than older methods. Our work serves as a cautionary note against taking results in ML-based science at face value. Kapoor and Narayanan show that leakage is a widespread failure mode in machine-learning (ML)-based science. Based on a survey of past reviews, they find that it affects at least 294 papers across 17 disciplines. They provide a