REFORMS: Consensus-based Recommendations for Machine-learning-based Science

This refers to the ability of an independent researcher to get the same results that are reported in a paper or manuscript. Computational reproducibility allows errors to be uncovered quickly. Independent researchers can verify and build on a study's results. Despite being a core tenet of computational research, it is hard to achieve computational reproducibility in practice.

This section helps readers and referees understand and evaluate the quality of the data used in the study. Using poor quality data or data that is not suitable for answering a research question can lead to results that are meaningless or misleading.

Different preprocessing steps can lead to vastly different outcomes from the modeling process. Even small changes, such as changing the order in which the data preparation steps take place, can lead to large differences in outcomes. For example, Vandewiele et al. find that oversampling before splitting the data into training and evaluation sets lead to widespread errors in pre-term birth detection.

There are many steps involved in developing an ML model. This makes it hard to report exact details about the model, and can hinder replication by independent researchers. For example, Raff found that ML results can often not be replicated using the paper's text (i.e., without using the code accompanying a paper). We ask authors to specify the main steps in the modeling process, including feature selection, the types of models considered, and evaluation.

Leakage is a spurious relationship between the independent variables and the target variable that arises as an artifact of the data collection, sampling, preprocessing, or modeling steps. For example, normalizing features in the training and test data together leads to leakage since information about the test data features is included in the training data. Leakage is a major source of reproducibility failures: it affects dozens of fields and hundreds of published papers and can lead to vastly overoptimistic results. We ask authors to justify that their study does not suffer from the main sources of leakage.

The performance of ML models is key to the scientific claims of interest. Since there are many possible choices authors can make when choosing metrics, it is important to reason why the metrics used are appropriate for the task at hand. Communicating and reasoning about uncertainty is critical. Still, Simmonds et al. find that studies often do not report the various kinds of uncertainty in the modeling process. We ask authors to report the metrics and uncertainty estimates used in enough detail to enable a judgment about whether they made valid choices for evaluating the performance of the model and drawing scientific inferences.

ML-based science faces a number of threats to external validity. Since studies that use ML methods are often unaccompanied by external (i.e., out-of-distribution) validation, it is important to reason about these threats. Additionally, authors are best positioned to identify the boundaries of applicability of their claims in order to prevent misunderstandings about the claims made in their study.