  The fast-paced development of machine learning (ML) methods coupled with its
increasing adoption in research poses challenges for researchers without
extensive training in ML. In neuroscience, for example, ML can help understand
brain-behavior relationships, diagnose diseases, and develop biomarkers using
various data sources like magnetic resonance imaging and
electroencephalography. The primary objective of ML is to build models that can
make accurate predictions on unseen data. Researchers aim to prove the
existence of such generalizable models by evaluating performance using
techniques such as cross-validation (CV), which uses systematic subsampling to
estimate the generalization performance. Choosing a CV scheme and evaluating an
ML pipeline can be challenging and, if used improperly, can lead to
overestimated results and incorrect interpretations.
  We created julearn, an open-source Python library, that allow researchers to
design and evaluate complex ML pipelines without encountering in common
pitfalls. In this manuscript, we present the rationale behind julearn's design,
its core features, and showcase three examples of previously-published research
projects that can be easily implemented using this novel library. Julearn aims
to simplify the entry into the ML world by providing an easy-to-use environment
with built in guards against some of the most common ML pitfalls. With its
design, unique features and simple interface, it poses as a useful Python-based
library for research projects.
