Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models

TL;DR

Julearn is a user-friendly Python library designed to help researchers evaluate machine learning models accurately by preventing common pitfalls in model validation, especially in neuroscience applications.

Contribution

The paper introduces julearn, a new open-source library that simplifies ML pipeline evaluation and guards against common validation errors, enhancing research reliability.

Findings

Facilitates complex ML pipeline design without common pitfalls

Provides built-in safeguards for validation procedures

Eases implementation of research projects using ML

Abstract

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.