Fig. 2.

Out-of-bag classification error (black points) and 95% confidence intervals (bars) for random forest classification models as a forest plot. Grey and white points are classification errors for atorvastatin and placebo arm, respectively; the bars are 95% confidence intervals. The confidence interval is for the Monte Carlo error. The vertical dotted line represents the 50% classification error, i.e., as-good-as-coin-flip; Out-of-bag classification error below 50% can be considered better model than random. Serum steroidomic hormone profile after the intervention classifies the treatment arms well. In the prostatic tissue, reduced model, with 11KDHT, DHEA, Estrone, and Testosterone as classifiers, classified the treatment arms with moderately low prediction error, whereas using all features failed in the classification task. For the serum, the sample sizes are n = 52 placebo and n = 56 atorvastatin. For the tissue, the sample sizes are n = 48 placebo and n = 51 atorvastatin.