Abstract Machine learning (ML) is increasingly used in cognitive, computational and clinical
neuroscience. The reliable and efficient application of ML requires a sound understanding of
its subtleties and limitations. Training ML models on datasets with imbalanced classes is a
particularly common problem, and it can have severe consequences if not adequately
addressed. With the neuroscience ML user in mind, this paper provides a didactic
assessment of the class imbalance problem and illustrates its impact through systematic …

Abstract Machine learning (ML) is becoming a standard tool in neuroscience and
neuroimaging research. Yet, because it is such a powerful tool, the appropriate application
of ML requires a sound understanding of its subtleties and limitations. In particular, applying
ML to datasets with imbalanced classes, which are very common in neuroscience, can have
severe consequences if not adequately addressed. With the neuroscience machine-learning
user in mind, this technical note provides a didactic overview of the class imbalance problem …