  For research to go in the right direction, it is essential to be able to
compare and quantify performance of different algorithms focused on the same
problem. Choosing a suitable evaluation metric requires deep understanding of
the pursued task along with all of its characteristics. We argue that in the
case of applied machine learning, proper evaluation metric is the basic
building block that should be in the spotlight and put under thorough
examination. Here, we address tasks with class imbalance, in which the class of
interest is the one with much lower number of samples. We encountered
non-insignificant amount of recent papers, in which improper evaluation methods
are used, borrowed mainly from the field of balanced problems. Such bad
practices may heavily bias the results in favour of inappropriate algorithms
and give false expectations of the state of the field.
