What Is Reproducibility?

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The ability to generate consistent findings utilizing the same input data, procedures, methods, code, and analytic settings is referred to as reproducibility. If something is reproducible in data science and machine learning, multiple individuals or teams can get the same results or conclusions using the same algorithms, tools, and dataset.

So, why is reproducibility necessary? It matters because it ensures the work done is trustworthy, leading to reliable results. Anyone can check the validity of results by using the available data, code, and tools to recreate the process.

Reproducibility benefits research teams because it:

  • Promotes the collaboration and review process 
  • Lessens errors and bugs, 
  • Prevents misinformation, 
  • Reduces duplication of work and efforts, 
  • Ensures continuity by building upon pre-existing work.  

 

Repeatability vs. Reproducibility

With numerous confusing definitions of reproducibility, it is easy to mistake it for repeatability. However, they are different.

Repeatability pertains to getting the same results under the same conditions—codes, tools, and methods—by the same team or person. If something is repeatable, the developer can reliably repeat their work but still obtain the same outcomes.

On the other hand, reproducibility involves having an independent team or person working with available disclosed resources, including data, codes, tools, and methods, to get consistent results.    

 

How to Ensure Reproducibility

Making your projects reproducible is challenging. Adhering to best practices makes achieving reproducibility easier.

  • Use Version Control Tools: With version control tools, you can keep track of changes to your data, code, and workflow. Depending on the aspects you wish to track, available tools efficiently log alterations that affect reproducibility.
  • Document the Process: Allow quick reproduction of your work by putting into paper everything about it, including comments in the code or workflows, thought process, hypothesis, lifecycle changes, etc. Through proper documentation, others will understand your work better because of the provided insights.   
  • Consider the Environment: Reproducibility is difficult when using different computational conditions or environments. For example, a different version of Python or R will result in another outcome even with the same script. Use containers like Dockers or virtual machines to run your project and include a list of packages and versions used.  

 

Achieve Reproducibility with Pachyderm

Make all your machine learning models reproducible with Pachyderm. It features best-in-class version control and data lineage to keep track of changes at any stage in the ML life cycle easily and efficiently. Try Pachyderm for free today and maintain reproducibility with minimal effort.

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