  This article explores the extension of well-known F1 score used for assessing
the performance of binary classifiers. We propose the new metric using
probabilistic interpretation of precision, recall, specificity, and negative
predictive value. We describe its properties and compare it to common metrics.
Then we demonstrate its behavior in edge cases of the confusion matrix.
Finally, the properties of the metric are tested on binary classifier trained
on the real dataset.
