Decision trees are a simple visual method for evaluating or classifying data, where each node consists of a variable in the dataset. Each node leads to a leaf in which the desired output is issued. A random forest is a combination of decision trees, which are randomly sampled, and the nodes are randomly organized. We split our dataset into training-, and test sets, as shown in Figure . In this case, Models A and B consisted of random forest classifications (RFC) performed with the 'randomForest' package in R. Both models were previously refined using the tuneRF function within the package, which chooses the optimal mtry variable. This value indicates the number of features selected at each split in each decision tree, where mtry = 2 provided the best prediction for both models (number of trees = 500, see Supporting Information Figure ). The importance of each descriptor in both models was evaluated through the mean decrease in the Gini impurity index using the MeanDecreaseGini function (Figure ).