In the context of low-dimensional data (i.e. when the number of covariates is small compared to the sample size), logistic regression is considered a standard approach for binary classification. This is especially true in scientific fields such as medicine or psycho-social sciences where the focus is not only on prediction but also on explanation; see Shmueli [1] for a discussion of this distinction. Since its invention 17 years ago, the random forest (RF) prediction algorithm [2], which focuses on prediction rather than explanation, has strongly gained popularity and is increasingly becoming a common “standard tool” also used by scientists without any strong background in statistics or machine learning. Our experience as authors, reviewers and readers is that random forest can now be used routinely in many scientific fields without particular justification and without the audience strongly questioning this choice. While its use was in the early years limited to innovation-friendly scientists interested (or experts) in machine learning, random forests are now more and more well-known in various non-computational communities.
