1. At least one computer program provided on at least one non-transitory computer readable storage medium and comprising code that when executed causes at least one computer to perform a method comprising: training a machine learning classifier with a set of labeled training data, such that the trained classifier is operable to determine a score with respect to the classification of cases for a category; determining at least one first distribution of scores for positive cases in the training set, wherein a positive case is a case that belongs to the category; determining at least one second distribution of scores for negative cases in the training set, wherein a negative case is a case that does not belong to the category; determining a third distribution of scores generated by the classifier classifying cases in a set of target data; and estimating a proportion of cases in the target set that are positive cases by fitting the at least one first distribution and the at least one second distribution to the third distribution.
4. The at least one computer program of claim 1 , wherein determining a proportion of the cases that are positive cases further comprises: determining a mixture comprising a weighted combination of the at least one first and at least one second distributions based on evaluating a fitting of the mixture to the third distribution; and determining the proportion of cases based on the weights associated in the determined mixture with the at least one first distribution.