JuRSE Code of the Month - May 2025

Each month we highlight a code from Forschungszentrum Jülich and this month's code is developed at the Institute of Neurosciences and Medicine, Brain and Behaviour (INM-7).

Julearn
Julearn aims to provide an easy-to-use but flexible interface to build predictive models with cross-validation (CV) consistent performance estimates. It poses as a solution accessible to domain experts without extensive ML training, enabling them to quickly fit and evaluate ML algorithms.

JuRSE selected highlights
Julearn provides a framework for conducting studies that simplifies machine learning for researchers. It provides an abstraction layer to state of the art tools, allowing researchers to easily build complex ML pipelines. The documentation includes introductory information to ML as well as examples relevant to the search for scientific insight.

More information
Website: https://juaml.github.io/julearn/main/index.html
RSD: https://helmholtz.software/software/julearn
GitHub: https://github.com/juaml/julearn