  The random forest algorithm (RF) has several hyperparameters that have to be
set by the user, e.g., the number of observations drawn randomly for each tree
and whether they are drawn with or without replacement, the number of variables
drawn randomly for each split, the splitting rule, the minimum number of
samples that a node must contain and the number of trees. In this paper, we
first provide a literature review on the parameters' influence on the
prediction performance and on variable importance measures.
  It is well known that in most cases RF works reasonably well with the default
values of the hyperparameters specified in software packages. Nevertheless,
tuning the hyperparameters can improve the performance of RF. In the second
part of this paper, after a brief overview of tuning strategies we demonstrate
the application of one of the most established tuning strategies, model-based
optimization (MBO). To make it easier to use, we provide the tuneRanger R
package that tunes RF with MBO automatically. In a benchmark study on several
datasets, we compare the prediction performance and runtime of tuneRanger with
other tuning implementations in R and RF with default hyperparameters.
