  In this note I study how the precision of a classifier depends on the ratio
$r$ of positive to negative cases in the test set, as well as the classifier's
true and false positive rates. This relationship allows prediction of how the
precision-recall curve will change with $r$, which seems not to be well known.
It also allows prediction of how $F_{\beta}$ and the Precision Gain and Recall
Gain measures of Flach and Kull (2015) vary with $r$.
