×

zbMATH — the first resource for mathematics

OpenML: an R package to connect to the machine learning platform openml. (English) Zbl 07095922
Summary: 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 [B. Bischl et al., J. Mach. Learn. Res. 17, Paper No. 170, 5 p. (2016; Zbl 1392.68007)]. 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.
MSC:
62-XX Statistics
65-XX Numerical analysis
PDF BibTeX Cite
Full Text: DOI
References:
[1] Asuncion A, Newman DJ (2007) UCI Machine Learning Repository. University of California, School of Information and Computer Science
[2] Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: Massive online analysis. J Mach Learn Res 11:1601-1604 http://www.jmlr.org/papers/v11/bifet10a.html
[3] Bischl B, Lang M (2015) parallelMap: Unified Interface to Parallelization Back-Ends. https://CRAN.R-project.org/package=parallelMap, r package version 1.3
[4] Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, Casalicchio G, Jones ZM (2016) mlr: Machine learning in R. J Mach Learn Res 17(170):1-5, http://jmlr.org/papers/v17/15-066.html · Zbl 1392.68007
[5] Bischl B, Richter J, Bossek J, Horn D, Thomas J, Lang M (2017) mlrmbo: A modular framework for model-based optimization of expensive black-box functions. arXiv preprint arXiv:1703.03373
[6] Carpenter, J., May the best analyst win, Science, 331, 698-699, (2011)
[7] Casalicchio G, Bischl B, Kirchhoff D, Lang M, Hofner B, Bossek J, Kerschke P, Vanschoren J (2017) OpenML: Exploring machine learning better, together. https://CRAN.R-project.org/package=OpenML, R package version 1.3
[8] Feurer M, Springenberg JT, Hutter F (2015) Initializing bayesian hyperparameter optimization via meta-learning. In: AAAI, pp 1128-1135
[9] Hall MA, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newslett 11(1):10-18, http://www.cs.waikato.ac.nz/ml/weka/
[10] Hothorn, T.; Hornik, K.; Zeileis, A., Unbiased recursive partitioning: a conditional inference framework, J Comput Gr Stat, 15, 651-674, (2006)
[11] Kuhn M, Weston S, Coulter N, Culp M (2015) C50: C5.0 decision trees and rule-based models. https://CRAN.R-project.org/package=C50, R package version 0.1.0-24, C code for C5.0 by R. Quinlan
[12] Lang, M.; Kotthaus, H.; Marwedel, P.; Weihs, C.; Rahnenführer, J.; Bischl, B., Automatic model selection for high-dimensional survival analysis, J Stat Comput Simul, 85, 62-76, (2015)
[13] Lang M, Bischl B, Surmann D (2017) batchtools: Tools for r to work on batch systems. J Open Source Softw 2(10), https://doi.org/10.21105%2Fjoss.00135
[14] Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18-22, http://CRAN.R-project.org/doc/Rnews/
[15] Nielsen M(2012) Reinventing discovery: the new era of networked science. Princeton University Press, http://www.jstor.org/stable/j.ctt7s4vx
[16] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825-2830, http://scikit-learn.org/ · Zbl 1280.68189
[17] Post MJ, van der Putten P, van Rijn JN (2016) Does feature selection improve classification? a large scale experiment in OpenML. In: International Symposium on Intelligent Data Analysis, Springer, pp 158-170
[18] Probst P, Au Q, Casalicchio G, Stachl C, Bischl B (2017) Multilabel classification with R package mlr. arXiv preprint arXiv:1703.08991
[19] R Core Team (2016) R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria, https://www.R-project.org/
[20] Schiffner J, Bischl B, Lang M, Richter J, Jones ZM, Probst P, Pfisterer F, Gallo M, Kirchhoff D, Kühn T, Thomas J, Kotthoff L (2016) mlr Tutorial. arXiv preprint arXiv:1609.06146
[21] Therneau T, Atkinson B, Ripley B (2015) rpart: Recursive Partitioning and Regression Trees. http://CRAN.R-project.org/package=rpart, R package version 4.1-10
[22] van Rijn JN, Umaashankar V, Fischer S, Bischl B, Torgo L, Gao B, Winter P, Wiswedel B, Berthold MR, Vanschoren J (2013) A RapidMiner Extension for Open Machine Learning. In: Proceedings of the 4th RapidMiner Community Meeting and Conference (RCOMM 2013), pp 59-70
[23] Vanschoren, J.; Blockeel, H.; Pfahringer, B.; Holmes, G., Experiment Databases. A new way to share, organize and learn from experiments, Mach Learn, 87, 127-158, (2012) · Zbl 1238.68134
[24] Vanschoren, J.; Rijn, JN; Bischl, B.; Torgo, L., OpenML: networked science in machine learning, SIGKDD Explor, 15, 49-60, (2013)
[25] Wickham H (2009) ggplot2: Elegant Graphics for Data Analysis. Springer, New York, NY, USA, http://ggplot2.org · Zbl 1170.62004
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.