  Classifiers are often tested on relatively small data sets, which should lead
to uncertain performance metrics. Nevertheless, these metrics are usually taken
at face value. We present an approach to quantify the uncertainty of
classification performance metrics, based on a probability model of the
confusion matrix. Application of our approach to classifiers from the
scientific literature and a classification competition shows that uncertainties
can be surprisingly large and limit performance evaluation. In fact, some
published classifiers are likely to be misleading. The application of our
approach is simple and requires only the confusion matrix. It is agnostic of
the underlying classifier. Our method can also be used for the estimation of
sample sizes that achieve a desired precision of a performance metric.
