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

By Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, B. Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl. 2017

64 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

To tune or not to tune the number of trees in random forest? (2017, methodology): “The datasets are downloaded with the help of the OpenML R package (Casalicchio et al., 2017).”
A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling (2019, methodology): “We analyzed datasets freely available on R software using the OpenML R package (website: https://www.openml.org/home) (Casalicchio, et al., 2017).”
OpenML Benchmarking Suites (2017, background): “OpenML offers bindings with the Java, Python and R ecosystems [van Rijn, 2016, Feurer et al., 2021b, Casalicchio et al., 2017] to provide easy integration in common machine learning tools, workﬂows, and environments.”
Hyperparameters and tuning strategies for random forest (2018, methodology): “, 2017) and download it via the OpenML R package (Casalicchio et al., 2017).”
OpenML Benchmarking Suites and the OpenML100 (2017, unknown): “, 2011), the R package (Casalicchio et al., 2017) with mlr (Bischl et al.”
Tunability: Importance of Hyperparameters of Machine Learning Algorithms (2018, methodology): “These search strategies range from simple grid or random search (Bergstra and Bengio, 2012) to more complex, iterative procedures such as Bayesian optimization (Hutter et al., 2011; Snoek et al., 2012; Bischl et al., 2017) or iterated F-racing (Birattari et al., 2010; Lang et al., 2017).”