  One of the significant problems associated with imbalanced data
classification is the lack of reliable metrics. This runs primarily from the
fact that for most real-life (as well as commonly used benchmark) problems, we
do not have information from the user on the actual form of the loss function
that should be minimized. Although it is pretty common to have metrics
indicating the classification quality within each class, for the end user, the
analysis of several such metrics is then required, which in practice causes
difficulty in interpreting the usefulness of a given classifier. Hence, many
aggregate metrics have been proposed or adopted for the imbalanced data
classification problem, but there is still no consensus on which should be
used. An additional disadvantage is their ambiguity and systematic bias toward
one class. Moreover, their use in analyzing experimental results in recognition
of those classification models that perform well for the chosen aggregated
metrics is burdened with the drawbacks mentioned above. Hence, the paper
proposes a simple approach to analyzing the popular parametric metric
$F_\beta$. We point out that it is possible to indicate for a given pool of
analyzed classifiers when a given model should be preferred depending on user
requirements.
