Effect of hyperparameters on variable selection in random forests
Cesaire J. K. Fouodo Lea L. Kronziel Inke R. König Silke Szymczak
Random forests (RFs) are well suited for prediction modeling and variable
selection in high-dimensional omics studies. The effect of hyperparameters of
the RF algorithm on prediction performance and variable importance estimation
have previously been investigated. However, how hyperparameters impact RF-based
variable selection remains unclear. We evaluate the effects on the Vita and the
Boruta variable selection procedures based on two simulation studies utilizing
theoretical distributions and empirical gene expression data. We assess the
ability of the procedures to select important variables (sensitivity) while
controlling the false discovery rate (FDR). Our results show that the
proportion of splitting candidate variables (mtry.prop) and the sample fraction
(sample.fraction) for the training dataset influence the selection procedures
more than the drawing strategy of the training datasets and the minimal
terminal node size. A suitable setting of the RF hyperparameters depends on the
correlation structure in the data. For weakly correlated predictor variables,
the default value of mtry is optimal, but smaller values of sample.fraction
result in larger sensitivity. In contrast, the difference in sensitivity of the
optimal compared to the default value of sample.fraction is negligible for
strongly correlated predictor variables, whereas smaller values than the
default are better in the other settings. In conclusion, the default values of
the hyperparameters will not always be suitable for identifying important
variables. Thus, adequate values differ depending on whether the aim of the
study is optimizing prediction performance or variable selection.