  Positive-Unlabelled (PU) learning is a growing area of machine learning that
aims to learn classifiers from data consisting of labelled positive and
unlabelled instances. Whilst much work has been done proposing methods for PU
learning, little has been written on the subject of evaluating these methods.
Many popular standard classification metrics cannot be precisely calculated due
to the absence of fully labelled data, so alternative approaches must be taken.
This short commentary paper critically reviews the main PU learning evaluation
approaches and the choice of predictive accuracy measures in 51 articles
proposing PU classifiers and provides practical recommendations for
improvements in this area.
