  The traditional binary classification framework constructs classifiers which
may have good accuracy, but whose false positive and false negative error rates
are not under users' control. In many cases, one of the errors is more severe
and only the classifiers with the corresponding rate lower than the predefined
threshold are acceptable. In this study, we combine binary classification with
statistical hypothesis testing to control the target error rate of already
trained classifiers. In particular, we show how to turn binary classifiers into
statistical tests, calculate the classification p-values, and use them to limit
the target error rate.
