Editor’s Assessment

This Technical Release (Software) paper presents Julearn, an open-source Python library, that allow neuroscience researchers to design and evaluate complex machine learning (ML) pipelines without encountering in common pitfalls such as data leakage and overfitting of hyperparameters. Created to be easy-to-use, accessible for researchers with diverse backgrounds, and to create reproducible results. Bridging the gap between domain expertise in neuroscience and application of ML pipelines. Towards that goal, julearn provides a simple interface only using two key API points. After some debugging and improvements to the documentation testing and review was positive, and a few useful examples are provided in the paper. Additional functionalities are also provided to guide and help users to inspect and evaluate the resulting cross validation scores.