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

2025-07-23

AI Summary

To address the critical challenges of opacity, lack of traceability, and poor cross-domain interpretability in machine learning experiments on tabular scientific data, this paper introduces Helix 1.0—a lightweight, open-source, and extensible Python framework. Helix systematically logs and automates the end-to-end ML workflow, including data preprocessing, modeling (with native scikit-learn integration), evaluation, and model interpretation (via SHAP and LIME). It ensures full reproducibility and auditability of analytical decisions. A key innovation is its natural-language-based explanation module, which translates model predictions into human-readable, domain-agnostic narratives—significantly enhancing comprehension and trust among non-expert researchers. Designed with usability and interoperability in mind, Helix adheres to FAIR principles and is distributed under the MIT License. It is publicly available on PyPI and GitHub, fostering transparent, collaborative, and reproducible scientific computing practices. (149 words)

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Security and Privacy: Data transparency and provenance
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Facilitates reproducible ML workflows for tabular data
• Ensures transparent analytics provenance and documentation
• Empowers non-experts with user-friendly ML interpretation

Innovation

• Open-source Python framework for reproducible ML
• Standardized preprocessing and interpretable workflows
• User-friendly interface with linguistic interpretation