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flowengineR framework enhances ML workflow reproducibility in R

By PulseAugur Editorial · [1 sources] ·

A new R package named flowengineR has been developed to create reproducible and extensible workflows for machine learning pipelines. This framework is particularly motivated by the challenges in algorithmic fairness, where new metrics and methods are constantly emerging. flowengineR offers a standardized architecture for various stages of the ML pipeline, including data splitting, preprocessing, training, and evaluation, allowing for easy comparison and integration of different interventions. The design draws inspiration from workflow languages and visual programming tools, prioritizing transparency and auditability over just parallel execution. AI

IMPACT Enhances reproducibility and extensibility in machine learning workflows, particularly for fairness research.

RANK_REASON This is a research paper describing a new software framework for machine learning workflows. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

flowengineR framework enhances ML workflow reproducibility in R

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This is a research paper describing a new software framework for machine learning workflows. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Maximilian Willer, Peter Ruckdeschel ·

    flowengineR: A Modular and Extensible Framework for Fair and Reproducible Workflow Design in R

    arXiv:2511.00079v2 Announce Type: replace Abstract: flowengineR is an R package designed to provide a modular and extensible framework for building reproducible algorithmic workflows for general-purpose machine learning pipelines. It is motivated by the rapidly evolving field of …

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