2024/03/07 by Sami Hamdan, Shammi More, Leonard Sasse +4 · 1 voice
Computer Science · Materials Science · Neuroscience · #Explainable Artificial Intelligence (XAI) #Functional Brain Connectivity Studies #Machine Learning in Materials Science
paper · pdf · doi:10.46471/gigabyte.113
openalex publication_date 2024/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The fast-paced development of machine learning (ML) and its increasing adoption in research challenge researchers without extensive training in ML. In neuroscience, ML can help understand brain-behavior relationships, diagnose diseases and develop biomarkers using data from sources like magnetic resonance imaging and electroencephalography. Primarily, ML builds models to make accurate predictions on unseen data. Researchers evaluate models' performance and generalizability using techniques such as cross-validation (CV). However, choosing a CV scheme and evaluating an ML pipeline is challenging and, if done improperly, can lead to overestimated results and incorrect interpretations. Here, we created julearn, an open-source Python library allowing researchers to design and evaluate complex ML pipelines without encountering common pitfalls. We present the rationale behind julearn's design, its core features, and showcase three examples of previously-published research projects. Julearn simplifies the access to ML providing an easy-to-use environment. With its design, unique features, simple interface, and practical documentation, it poses as a useful Python-based library for research projects.