  State-of-the-art machine learning algorithms demonstrate close to absolute
performance in selected challenges. We provide arguments that the reason can be
in low variability of the samples and high effectiveness in learning typical
patterns. Due to this fact, standard performance metrics do not reveal model
capacity and new metrics are required for the better understanding of
state-of-the-art.
