RF is one of the so-called ensemble methods relying on decision trees. In RF classification, a fixed number of decision tree classifiers are fitted on various bootstrapped subsamples of the training dataset. For a given sample, each decision tree predicts a label, and the final prediction of the sample is the label predicted by most of the trees. For this algorithm, the number of decision trees was fixed to 1000 and the number of features to consider when searching for the best splits in the individual trees was optimized in a representationdependent manner using candidate values of 1, 2, 3, 4 and 5 times the square root of the number of features in the fingerprint representation.