  The use of machine learning (ML) methods for prediction and forecasting has
become widespread across the quantitative sciences. However, there are many
known methodological pitfalls, including data leakage, in ML-based science. In
this paper, we systematically investigate reproducibility issues in ML-based
science. We show that data leakage is indeed a widespread problem and has led
to severe reproducibility failures. Specifically, through a survey of
literature in research communities that adopted ML methods, we find 17 fields
where errors have been found, collectively affecting 329 papers and in some
cases leading to wildly overoptimistic conclusions. Based on our survey, we
present a fine-grained taxonomy of 8 types of leakage that range from textbook
errors to open research problems.
  We argue for fundamental methodological changes to ML-based science so that
cases of leakage can be caught before publication. To that end, we propose
model info sheets for reporting scientific claims based on ML models that would
address all types of leakage identified in our survey. To investigate the
impact of reproducibility errors and the efficacy of model info sheets, we
undertake a reproducibility study in a field where complex ML models are
believed to vastly outperform older statistical models such as Logistic
Regression (LR): civil war prediction. We find that all papers claiming the
superior performance of complex ML models compared to LR models fail to
reproduce due to data leakage, and complex ML models don't perform
substantively better than decades-old LR models. While none of these errors
could have been caught by reading the papers, model info sheets would enable
the detection of leakage in each case.
