JesperDramsch/ml.recipes
Increase citations, ease review & collaboration A collection of "easy wins" to make machine learning in research reproducible. This tutorial focuses on basics that work. Getting you 90% of the way to top-tier reproducibility.
This project provides practical guidance and code examples to improve the quality and reproducibility of machine learning applications in scientific research. It helps researchers who use machine learning in their work to structure their projects and code, making it easier for others to understand, use, and build upon their findings. The output is more robust research that is easier to publish and more likely to be cited.
Use this if you are a scientist, academic, or researcher using machine learning and want to ensure your work is easily understood, reproducible, and citable by your peers.
Not ideal if you are looking for advanced machine learning algorithms or a deep dive into specific model architectures rather than best practices for research reproducibility.
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HTML
License
MIT
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Last pushed
Nov 21, 2025
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