The performance of RF is known to be relatively robust against parameter specifications: performance generally depends less on parameter values than for other machine learning algorithms [19]. However, noticeable improvements may be achieved in some cases [20]. The recent R package tuneRanger [4] allows to automatically tune RF’s parameters simultaneously using an efficient model-based optimization procedure. In additional analyses presented in “Additional analysis: tuned RF” section, we compare the performance of RF and LR with the performance of RF tuned with this procedure (denoted as TRF).
