It can be observed from Fig. 5 that RF tends to yield better results than LR for a low n, and that the difference decreases with increasing n. In contrast, RF performs comparatively poorly for datasets with p<5, but better than LR for datasets with p≥5. This is due to low performances of RF on a high proportion of the datasets with p<5. For \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 difference between RF and LR is negligible in low dimension \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\left (\frac {p}{n}<0.01\right)$\end{document}pn<0.01, but increases with the dimension. The contrast is particularly striking between the subgroups \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\frac {p}{n}<0.1$\end{document}pn<0.1 (yielding a small Δacc) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\frac {p}{n}\geq 0.1$\end{document}pn≥0.1 (yielding a high Δacc), again confirming the hypothesis that the superiority of RF over LR is more pronounced for larger dimensions.
