Metadata Repositories

Pavel Brazdil; Jan N. van Rijn; Carlos Soares; Joaquin Vanschoren

doi:10.1007/978-3-030-67024-5_16

Metadata Repositories

Pavel Brazdil (Corresponding author), Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

1 Citation (Scopus)

Abstract

This chapter presents a review of online repositories where researchers can share data, code, and experiments. In particular, it covers OpenML, an online platform for sharing and organizing machine learning data automatically. OpenML contains thousands of datasets and algorithms, and millions of experimental results. We describe the basic philosophy involved, and its basic components: datasets, tasks, flows, setups, runs, and benchmark suites. OpenML has API bindings in various programming languages, making it easy for users to interact with the API in their native language. One important feature of OpenML is the integration into various machine learning toolboxes, such as Scikit-learn, Weka, and mlR. Users of these toolboxes can automatically upload all their results, leading to a large repository of experimental results.

Original language	English
Title of host publication	Metalearning
Subtitle of host publication	Applications to Automated Machine Learning and Data Mining
Publisher	Springer
Chapter	16
Pages	297-310
Number of pages	14
ISBN (Electronic)	978-3-030-67024-5
ISBN (Print)	978-3-030-67023-8
DOIs	https://doi.org/10.1007/978-3-030-67024-5_16
Publication status	Published - 2022

Publication series

Name	Cognitive Technologies
ISSN (Print)	1611-2482
ISSN (Electronic)	2197-6635

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

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10.1007/978-3-030-67024-5_16

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AB - This chapter presents a review of online repositories where researchers can share data, code, and experiments. In particular, it covers OpenML, an online platform for sharing and organizing machine learning data automatically. OpenML contains thousands of datasets and algorithms, and millions of experimental results. We describe the basic philosophy involved, and its basic components: datasets, tasks, flows, setups, runs, and benchmark suites. OpenML has API bindings in various programming languages, making it easy for users to interact with the API in their native language. One important feature of OpenML is the integration into various machine learning toolboxes, such as Scikit-learn, Weka, and mlR. Users of these toolboxes can automatically upload all their results, leading to a large repository of experimental results.

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Metadata Repositories

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