We devoted particular attention to the inclusion criteria applied when selecting datasets for our study. We investigated how the conclusions of our benchmark experiment change in different subgroups of datasets. Our analyses reveal a noticeable influence of the number of features p and the ratio \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\frac {p}{n}$\end{document}pn. The superiority of RF tends to be more pronounced for increasing p and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\frac {p}{n}$\end{document}pn. More generally, our study outlines the importance of inclusion criteria and the necessity to include a large number of datasets in benchmark studies as outlined in previous literature [11, 28, 31].
