MSE of the partial correlation matrix estimates.

<p> and when the graphs are decomposable.</p

Additional file 3 of Random forest versus logistic regression: a large-scale benchmark experiment

Results on partial dependence. Additional file 3 includes a study on interesting extreme cases that allows to gain more insight into the behaviour of LR and RF using partial dependence plots defined in “Partial dependence plots” section. (PDF 256 kb

Performance of iPACOSE (black straight lines) when compared to its regression based GGM estimate counterpart (red dashed lines).

<p><i>(a) and (b)</i>: performance of the LASSO version of iPACOSE. and for and (a) and for and (b). Thresholds: , , and . The results of iPACOSE are represented by the black line and the results of the adalasso.net function with the red dashed line. <i>UPPER FIGURE</i>: sensitivity as a function of the threshold, <i>LOWER FIGURE</i>: PPV as a function of the threshold. <i>(c) and (d)</i>: performance of the adaptive LASSO version of iPACOSE. and for and (c) and for and (d). Thresholds: , , and . The results of iPACOSE are represented by the black line and the results of the adalasso.net function with the red dashed line. <i>UPPER FIGURE</i>: sensitivity as a function of the threshold, <i>LOWER FIGURE</i>: PPV as a function of the threshold.</p

Measure of the stability with Fleiss' for the methods ridge.net and the Ridge version of iPACOSE.

<p><i>LEFT FIGURE</i>: and . <i>RIGHT FIGURE</i>: and . The regularization parameter of the ridge regression is determined analytically <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060536#pone.0060536-Hoerl2" target="_blank">[26]</a> for both methods.</p

Flowchart representation of the iPACOSE algorithm, representing how it iteratively uses PACOSE to estimate an independence graph from a dataset.

<p>Flowchart representation of the iPACOSE algorithm, representing how it iteratively uses PACOSE to estimate an independence graph from a dataset.</p

Additional file 1 of Random forest versus logistic regression: a large-scale benchmark experiment

Additional results of subgroup analyses. Additional file 1 extends Fig. 5 for all considered measures, and include the outliers. (PDF 203 kb

MSE of the partial correlation matrix estimates.

<p> and when the graphs are not decomposable. Since the graphs are not decomposable, the estimators MVUE and SURE are not applicable. Wermuth's algorithm does not converge, and the implementation of Whittaker's method requires a decomposition of the graph into cliques.</p

Additional file 4 of Random forest versus logistic regression: a large-scale benchmark experiment

Results with tuned random forest (TRF). Additional file 4 shows the results of the comparison study between LR, RF and TRF based on the 67 datasets from biosciences/medicine. (PDF 224 kb

Performance of iPACOSE (black straight lines) when compared to its regression based GGM estimate counterpart (red dashed lines).

<p><i>(a) and (b)</i>: performance of the PLS version of iPACOSE. and for and (a) and for and (b). Thresholds: , , and . The results of iPACOSE are represented by the black line and the results of the pls.net function with the red dashed line. <i>UPPER FIGURE</i>: sensitivity as a function of the threshold, <i>LOWER FIGURE</i>: PPV as a function of the threshold. <i>(c) and (d)</i>: performance of the Ridge version of iPACOSE. and for and (c) and for and (d). Thresholds: , , and . The results of iPACOSE are represented by the black line and the results of the ridge.net function with the red dashed line. <i>UPPER FIGURE</i>: sensitivity as a function of the threshold, <i>LOWER FIGURE</i>: PPV as a function of the threshold.</p

Prediction nomenclature in the context of graph inference.

<p>The definitions of true and false positives (resp. TP and FP), true and false negatives (resp. TN and FN) in the context of graph inference.</p