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 not just using scikit-learn? Julearn offers three essential benefits:
You can do machine learning with less amount of code than in
Julearn helps you to build and evaluate pipelines in an easy way and thereby
helps you avoid data leakage!
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.