Scikit-Longitudinal, also called Sklong , a Scikit-Learn API compliant Python library for Longitudinal Data Analysis, with a focus on Longitudinal ML Classification task
Get Started 🚀
Checkout our Automated machine learning repository for Scikit-Longitudinal, Auto-Sklong!
Paper is incoming. Submitted to a conference.
What's Sklong? ¶
Your Longitudinal Analysis in ~10 lines of code
Sklong aims to be simple to use and understand. By following the popular Fit/Predict/Transform API style of Scikit-learn, we allow any user already familiar with the library to take up to a couple of seconds to use the library, and for newcomers a couple of minutes, as Scikit-learn is the, state-of-the-art in the ease of use and standard machine learning algorithms in the community today.
Sklong allows you to train classifiers on longitudinal data, not simply by flattening
your longitudinal tabular data and running standard machine learning algorithms. Instead, we create
and adapt longitudinal-focused (new/variant) machine learning algorithms for longitudinal
tabular data classification, allowing your temporal dependencies to be fully exploited 🎉
As well as, providing sophisticated data transformation (flattening) techniques to still apply standard
machine learning algorithms if needed.
Your data's temporal dependency matter
Scikit-Longitudinal comes up with a very easy and simple way to understand the temporal dependencies of your data. We rely on a (user-)defined matrix representation of which feature indices are both temporal and or non-temporal. Which hence for temporal features how recent they are. Therefore, by conveying to this matrix, we let algorithms' designers with the ability to deal with the temporal dependencies of the data the way they designed their approach to cope with longitudinal data classification using machine learning.
Open Source Software at the core of our team
Scikit-Longitudinal is an open source project. We believe in open-source software and the community that supports it. We are dedicated to contributing to the open-source community and working with other projects. Please contribute your longitudinal machine learning algorithms to this initiative.
Who's our team? ¶
Simon Provost
Main Author of Scikit-Longitudinal & Ph.D Student
@ University of Kent
Simon designed, developed and maintain the Scikit-Longitudinal library. Simon is a Ph.D student in computer science at the University of Kent, United-Kingdom. His research interests are in Machine Learning (ML), Automated Machine Learning and ML-applied Healthcare.
Prof. Alex Freitas
Professor of Computational Intelligence, Head of Research
@ University of Kent
Alex is a Professor of Computational Intelligence at the University of Kent, United-Kingdom. He is the main supervisor of Simon's Ph.D., and also designed the Scikit-Longitudinal library with. Alex's research interests are in machine learning, data mining, and bioinformatics.