OpenML is an online machine learning platform where researchers can automatically log and share data, code, and experiments, and organize them online to work and collaborate more effectively. We present an R package to interface the OpenML platform and illustrate its usage both as a stand-alone package and in combination with the mlr machine learning package. We show how the OpenML package allows R users to easily search, download and upload machine learning datasets. Users can easily log their auto ML experiment results online, have them evaluated on the server, share them with others and download results from other researchers to build on them. Beyond ensuring reproducibility of results, it automates much of the drudge work, speeds up research, facilitates collaboration and increases user’s visibility online. Currently, OpenML has 1,000+ registered users, 2,000+ unique monthly visitors, 2,000+ datasets, and 500,000+ experiments. The OpenML server currently supports client interfaces for Java, Python, .NET and R as well as specific interfaces for the WEKA, MOA, RapidMiner, scikit-learn and mlr toolboxes for machine learning.
Giuseppe Casalicchio is a PhD student in computational statistics at the Ludwig Maximilian University of Munich. He earned a Bachelor and Masters degree in statistics in 2011 and 2013 respectively and worked as a statistical consultant at the statistical consulting unit ‚StaBLab‘ from 2012 to 2015. Since 2014 he is giving R training courses for scientists and business clients at the ‚Department of Statistics – Munich R Courses‘. His research interests focus on optimizing machine learning algorithms, visualizing their predictive performance and gaining insights from predictive models.