RF belongs to non-linear ensemble classifiers , algorithms that follow a paradigm where multiple "weak classifiers", here decision trees, are trained and aggregated to improve accuracy. Aggregation is conducted by means of bootstrapping (each tree is trained on a part of data and a subset of features) and voting for the final class. The hyper-parameter space for an RF can be large and complex with parameters such as tree depth, number of trees, number of features, and class weights. The RF algorithm is fed with a matrix X (various chemical representations of the chemical compounds) and a vector y, which is a positive binding value. Optimization of the RF's parameters did not yield significant improvement on the cross-validation sets, thus all the RF parameters were set to their default values, apart from n_estimators which was set to 1000.