Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916860760424448 |
|---|---|
| author | Aguilar-Bejarano, Eduardo Lea, Daniel Sivakumar, Karthikeyan Mase, Jimiama M. Omidvar, Reza Li, Ruizhe Kettle, Troy Mitchell-White, James Alexander, Morgan R Winkler, David A Figueredo, Grazziela |
| author_facet | Aguilar-Bejarano, Eduardo Lea, Daniel Sivakumar, Karthikeyan Mase, Jimiama M. Omidvar, Reza Li, Ruizhe Kettle, Troy Mitchell-White, James Alexander, Morgan R Winkler, David A Figueredo, Grazziela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17791 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data Aguilar-Bejarano, Eduardo Lea, Daniel Sivakumar, Karthikeyan Mase, Jimiama M. Omidvar, Reza Li, Ruizhe Kettle, Troy Mitchell-White, James Alexander, Morgan R Winkler, David A Figueredo, Grazziela Machine Learning Artificial Intelligence 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. |
| title | Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.17791 |