Random forest classification analysis was performed using R-packages “randomForest” (https://cran.r-project.org/web/packages/randomForest), “caret” (https://cran.r-project.org/web/packages/caret), and “randomForestExplainer” (https://cran.r-project.org/web/packages/randomForestExplainer). A Random Forest classifier consists of a collection of tree-structured classifiers where each tree casts a unit vote for a class/group for each set of predictor variables [82]. A growing number of studies in computational biology are using RF because (i) it is nonparametric, interpretable, efficient, and (ii) it has high prediction accuracy for many types of data due to its unique advantages in dealing with a small sample size, high-dimensional feature space, and complex data structures [83]. The two main parameters of the random forest algorithm are the number of trees in the ensemble and the number of variables randomly selected for the splitting decision at each node. Two levels of randomness are used by the random forest to construct the ensemble of trees: first, the model trains itself using a training data for creating each tree based on bootstrap aggregating (bagging). At the second level, the algorithm randomly selects a subset of features to split at each node while growing a decision tree
