In the original version of RF [2], each tree of the RF is built based on a bootstrap sample drawn randomly from the original dataset using the CART method and the Decrease Gini Impuritiy (DGI) as the splitting criterion [2]. When building each tree, at each split, only a given number mtry of randomly selected features are considered as candidates for splitting. RF is usually considered a black-box algorithm, as gaining insight on a RF prediction rule is hard due to the large number of trees. One of the most common approaches to extract from the random forest interpretable information on the contribution of different variables consists in the computation of the so-called variable importance measures outlined in “Variable importance measures” section. In this study we use the package randomForest [3] (version 4.6-12) with default values, see the next paragraph for more details on tuning parameters.
