Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data

TL;DR

Helix 1.0 is an open-source Python framework designed to make machine learning on tabular data transparent, reproducible, and interpretable, with user-friendly tools for data processing, visualization, and decision explanation.

Contribution

It introduces a comprehensive, extensible platform that integrates data preprocessing, visualization, modeling, and interpretation within a user-friendly environment for scientific data analysis.

Findings

Supports reproducible workflows with detailed provenance

Includes a novel interpretation method using linguistic terms

Facilitates accessible and transparent data science practices

Abstract

Helix is an open-source, extensible, Python-based software framework to facilitate reproducible and interpretable machine learning workflows for tabular data. It addresses the growing need for transparent experimental data analytics provenance, ensuring that the entire analytical process -- including decisions around data transformation and methodological choices -- is documented, accessible, reproducible, and comprehensible to relevant stakeholders. The platform comprises modules for standardised data preprocessing, visualisation, machine learning model training, evaluation, interpretation, results inspection, and model prediction for unseen data. To further empower researchers without formal training in data science to derive meaningful and actionable insights, Helix features a user-friendly interface that enables the design of computational experiments, inspection of outcomes, including a novel interpretation approach to machine learning decisions using linguistic terms all within an integrated environment. Released under the MIT licence, Helix is accessible via GitHub and PyPI, supporting community-driven development and promoting adherence to the FAIR principles.