Making Complex Prediction Rules Applicable for Readers: Current Practice in Random Forest Literature and Recommendations

Ideally, prediction rules (including classifiers as a special case) should be published in such a way that readers may apply them, for example to make predictions for their own data. While this is straightforward for simple prediction rules, such as those based on the logistic regression model, this is much more difficult for complex prediction rules derived by machine learning tools. We conducted a survey of articles reporting prediction rules that were constructed using the random forest algorithm and published in PLOS ONE in 2014-2015 with the aim to identify issues related to their applicability. The presented prediction rules were applicable in only 2 of 30 identified papers, while for further 8 prediction rules it was possible to obtain the necessary information by contacting the authors. Various problems, such as non-response of the authors, hampered the applicability of prediction rules in the other cases. Based on our experiences from the survey, we formulate a set of recommendations for authors publishing complex prediction rules to ensure their applicability for readers.