5. A computer-implemented method, comprising: based on reference data that includes individuals of a population and labels that each indicate whether a given individual meets one or more specific criteria, generating a first machine learning model for determining whether individuals meet said specific criteria; identifying one or more false positive individuals, wherein a given false positive individual is an individual that the first machine learning model indicates as having meeting said specific criteria and which is labeled within the reference data as not meeting said specific criteria; selecting one or more of the identified false positive individuals as candidates to be corrected within the reference data; and subsequent to a correction of the reference data associated with one or more of the selected false positive individuals, generating based on the corrected reference data a new machine learning model for determining whether individuals meet said specific criteria.
9. The computer-implemented method of claim 5 , wherein one or more of the machine learning models include one or more of: a genetic algorithm (GA), a boosted decision tree (BDT) model, or a support vector machine (SVM) model.