  Medical researchers have solved the problem of estimating the sensitivity and
specificity of binary medical diagnostic tests without gold standard tests for
comparison. That problem is the same as estimating confusion matrices for
classifiers on unlabeled data. This article describes how to modify the
diagnostic test solutions to estimate confusion matrices and accuracy
statistics for supervised or unsupervised binary classifiers on unlabeled data.
