Fig. 1

ROC-AUC for various model structure variations. The blue line represents the evolution of APS when varying the number of decision trees in one single RF. The input for the RF was the entire set of positive instances and a balanced random sample (1:1 ratio) of negative instances. The green line represents the evolution of APS for a single balanced RF, where the number of decision trees composing the balanced RF varies. The input for each Balanced RF was the same full training data set (with it’s 1:500 imbalance ratio). The orange line represents the evolution of APS when varying the number of decision trees present in each of the 500 RFs in an ensemble RF model, similarly to the first version of VarCoPP. The input for each RF was the entire set of positive instances and an equal amount of negative instances, specific to each RF. The red line represents the evolution of APS for different numbers of RFs in an ensemble RF model. Each RF consisted of 100 decision trees and its input was the entire set of positive instances and an equal amount of negative instances, unique for each RF