OpenML: An R Package to Connect to the Machine Learning Platform OpenML
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Abstract (arXiv)
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. 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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2017.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 4; 2024: 0; 2025: 1; 2026: 1.
12 papers.
- 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
- 2022Interaction-Grounded Learning with Action-inclusive Feedbackcited by 7
- 2022On the role of benchmarking data sets and simulations in method comparison studiescited by 7
- 2021Test for non-negligible adverse shiftscited by 5
- 2023Self-Adjusting Weighted Expected Improvement for Bayesian Optimizationcited by 5
- 2022Accelerated and interpretable oblique random survival forestscited by 3
- 2021Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimizationcited by 2
- 2022Post-Selection Confidence Bounds for Prediction Performancecited by 2
- 2023Comprehensive Algorithm Portfolio Evaluation using Item Response Theorycited by 2
- 2025TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimizationcited by 2
- 2023Tiny Classifier Circuits: Evolving Accelerators for Tabular Datacited by 1
- 2026TNLearn: An Open Source Python Package for Task-based Neuronscited by 0
- 2026TNLearn: An Open Source Python Package for Task-based Neurons23 Sep 2026
- 2025TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization26 Feb 2025
- 2023Comprehensive Algorithm Portfolio Evaluation using Item Response Theory29 Jul 2023
- 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks13 Jun 2023
- 2023Self-Adjusting Weighted Expected Improvement for Bayesian Optimization7 Jun 2023
- 2023Tiny Classifier Circuits: Evolving Accelerators for Tabular Data28 Feb 2023
- 2022Post-Selection Confidence Bounds for Prediction Performance24 Oct 2022
- 2022On the role of benchmarking data sets and simulations in method comparison studies2 Aug 2022
- 2022Accelerated and interpretable oblique random survival forests1 Aug 2022
- 2022Interaction-Grounded Learning with Action-inclusive Feedback16 Jun 2022
- 2021Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization7 Oct 2021
- 2021Test for non-negligible adverse shifts7 Jul 2021