  Binary classifiers trained on a certain proportion of positive items
introduce a bias when applied to data sets with different proportions of
positive items. Most solutions for dealing with this issue assume that some
information on the latter distribution is known. However, this is not always
the case, certainly when this proportion is the target variable. In this paper
a maximum likelihood estimator for the true proportion of positives in data
sets is suggested and tested on synthetic and real world data.
