6. A system of generating a representative model for a plurality of different models identified by similar feature data, the system comprising: a memory; and one or more processors, the processors configured to receive a first model and a second model, each of the first model and the second model configured for use in identifying a second set of network user identifiers as similar to a first set of network user identifiers; receive feature data associated with each of the first model and the second model, each feature data having a corresponding weight data; select a network user identifier pool including a plurality of network user identifiers, a subset of the network user identifier pool including at least one network user identifier that is included in at least one of the second set of network user identifiers identified by at least one of the first model or the second model plurality of different models and at least one network user identifier that is not included in the at least one of the second set of network user identifiers identified by at least one of the plurality of different models; determine, for each model of the first model and the second model, from the network user identifier pool, a network user identifier identified as similar to the first set of network user identifiers of the model; determining an overlap between positive predictions and negative predictions of the first model and the second model, a positive prediction between the first model and the second model occurring when each of the first model and the second model identifies a network user identifier from the network user identifier pool as a similar network user and a negative prediction between the first model and the second model occurring when either the first model identifies a network user identifier from the network user identifier pool that is not identified by the second model or the second model identifies a network user identifier from the network user identifier pool that is not identified by the first model; and calculate, for the first model and the second model, a degree of overlap between the positive predictions and the negative predictions; identify that the first model and the second model are similar responsive to determining that the degree of overlap is greater than a threshold value; and generate the representative model to represent the first model and the second model, the representative model configured for use in generating a second set of network user identifiers associated with the representative model based on a first set of network user identifiers associated with the representative model.
9. The system of claim 6 , wherein generating the representative model comprises generating the representative model by ranking the feature data and the corresponding weight data associated with each of the first model and the second model and selecting a predetermined number of high-ranking feature data from each of the first model and the second model.