The need for open source software in machine learning

Sören Sonnenburg, Mikio L. Braun, Soon Ong Cheng, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander Smola, Pascal Vincent, Jason Weston, Robert C. Williamson

Research output: Contribution to journalArticlepeer-review


Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not used, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.

Original languageEnglish (US)
Pages (from-to)2443-2466
Number of pages24
JournalJournal of Machine Learning Research
StatePublished - Oct 2007


  • Algorithms
  • Creditability
  • Machine learning
  • Open source
  • Reproducibility
  • Software

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability


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