  For information retrieval and binary classification, we show that precision
at the top (or precision at k) and recall at the top (or recall at k) are
maximised by thresholding the posterior probability of the positive class. This
finding is a consequence of a result on constrained minimisation of the
cost-sensitive expected classification error which generalises an earlier
related result from the literature.
