To investigate these dependencies more deeply, we examine the performances of RF and LR within subgroups of datasets defined based on datasets’ meta-features (called meta-features from now on), following the principle of subgroup analyses well-known in clinical research. As some of the meta-features displayed in Table 3 are mutually (highly) correlated, we cluster them using a hierarchical clustering algorithm (data not shown). From the resulting dendogram we decide to select the meta-features p, n, \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, Cmax, while other meta-features are considered redundant and ignored in further analyses.
