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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.

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Cite this software

DOI:

10.5281/zenodo.8164422

Description

At the Applied Machine Learning AML group, as part of the Institute of
Neuroscience and Medicine - Brain and Behaviour INM-7, we thought that
using ML in research could be simpler.

In the same way as seaborn provides an abstraction of matplotlib's
functionality aiming for powerful data visualization with minor coding, we
built julearn on top of scikit-learn.

Julearn is a library that provides users with the possibility of easy
testing ML models directly from pandas dataframes, while keeping the
flexibiliy of using scikit-learn's models.

To get started with Julearn just keep reading here. Additionally You can
check out our video tutorial.

Why Julearn?

Why not just using scikit-learn? Julearn offers three essential benefits:

  1. You can do machine learning with less amount of code than in
    scikit-learn

  2. Julearn helps you to build and evaluate pipelines in an easy way and thereby
    helps you avoid data leakage!

  3. It offers you nice additional functionality:

    • Easy to implement confound removal: Julearn offers you a simple way
      to remove confounds from your data in a cross-validated way.
    • Data typing: Julearn provides a system to specify data types for
      your features, and then provides you with the possibility to
      filter and transform your data according to these types.
    • Model inspection: Julearn provides you with a simple way to inspect
      your models and pipelines, and thereby helps you to understand what is
      going on in your pipeline.
    • Model comparison: Julearn provides out-of-the-box interactive
      visualizations and statistics to compare your models.
Logo of Julearn
Keywords
Machine learning
Neuroimaging
python
Programming language
  • Python 100%
License
  • AGPL-3.0-or-later
</>Source code
Packages
pypi.org
anaconda.org

Participating organisations

Forschungszentrum Jülich

Reference papers

Mentions

Contributors

FR
Fede Raimondo
Forschungszentrum Jülich
SH
Sami Hamdan
VK
Vera Komeyer
SM
Shammi More
KP
Kaustubh Patil
LS
Leonard Sasse