With respect to ease of application, the results of the empirical comparisons between different supervised learning methods conducted by Caruana and Niculescu-Mizil (2006) and Svetnik et al. (2004) indicate that random forests are among the best performing methods even without extra tuning. Therefore random forests can be considered as a valuable “off the shelf” tool for exploring complex data sets, that may in a few years from now become as popular in psychology as it is now in the fields of genetics and bioinformatics.
