  Classification systems are evaluated in a countless number of papers.
However, we find that evaluation practice is often nebulous. Frequently,
metrics are selected without arguments, and blurry terminology invites
misconceptions. For instance, many works use so-called 'macro' metrics to rank
systems (e.g., 'macro F1') but do not clearly specify what they would expect
from such a `macro' metric. This is problematic, since picking a metric can
affect research findings, and thus any clarity in the process should be
maximized.
  Starting from the intuitive concepts of bias and prevalence, we perform an
analysis of common evaluation metrics. The analysis helps us understand the
metrics' underlying properties, and how they align with expectations as found
expressed in papers. Then we reflect on the practical situation in the field,
and survey evaluation practice in recent shared tasks. We find that metric
selection is often not supported with convincing arguments, an issue that can
make a system ranking seem arbitrary. Our work aims at providing overview and
guidance for more informed and transparent metric selection, fostering
meaningful evaluation.
