IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v34y2019i3d10.1007_s00180-017-0742-2.html
   My bibliography  Save this article

OpenML: An R package to connect to the machine learning platform OpenML

Author

Listed:
  • Giuseppe Casalicchio

    (Ludwig-Maximilians-University Munich)

  • Jakob Bossek

    (University of Münster)

  • Michel Lang

    (TU Dortmund University)

  • Dominik Kirchhoff

    (Dortmund University of Applied Sciences and Arts)

  • Pascal Kerschke

    (University of Münster)

  • Benjamin Hofner

    (Paul-Ehrlich-Institut)

  • Heidi Seibold

    (University of Zurich)

  • Joaquin Vanschoren

    (Eindhoven University of Technology)

  • Bernd Bischl

    (Ludwig-Maximilians-University Munich)

Abstract

OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1–5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online.

Suggested Citation

  • Giuseppe Casalicchio & Jakob Bossek & Michel Lang & Dominik Kirchhoff & Pascal Kerschke & Benjamin Hofner & Heidi Seibold & Joaquin Vanschoren & Bernd Bischl, 2019. "OpenML: An R package to connect to the machine learning platform OpenML," Computational Statistics, Springer, vol. 34(3), pages 977-991, September.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:3:d:10.1007_s00180-017-0742-2
    DOI: 10.1007/s00180-017-0742-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-017-0742-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-017-0742-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matthias Schmid & Bernd Bischl & Hans A. Kestler, 2019. "Proceedings of Reisensburg 2016–2017," Computational Statistics, Springer, vol. 34(3), pages 943-944, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:34:y:2019:i:3:d:10.1007_s00180-017-0742-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.