mlscorecheck: Testing the consistency of reported performance scores and
  experiments in machine learning
György Kovács Attila Fazekas
Addressing the reproducibility crisis in artificial intelligence through the
validation of reported experimental results is a challenging task. It
necessitates either the reimplementation of techniques or a meticulous
assessment of papers for deviations from the scientific method and best
statistical practices. To facilitate the validation of reported results, we
have developed numerical techniques capable of identifying inconsistencies
between reported performance scores and various experimental setups in machine
learning problems, including binary/multiclass classification and regression.
These consistency tests are integrated into the open-source package
mlscorecheck, which also provides specific test bundles designed to detect
systematically recurring flaws in various fields, such as retina image
processing and synthetic minority oversampling.