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PMLB v1.0: An open source dataset collection for benchmarking machine learning methods

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arxiv 2012.00058 v3 pith:SMIAWLPP submitted 2020-11-30 cs.LG cs.DB

Joseph D. Romano , Trang T. Le , William La Cava , John T. Gregg , Daniel J. Goldberg , Natasha L. Ray , Praneel Chakraborty , Daniel Himmelstein
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Weixuan Fu Jason H. Moore
This is my paper · ORCID
classification cs.LGcs.DB
keywords pmlbdatasetslearningmachinemethodsbenchmarkcollectiondata
verification ladder T0 review T1 audit T2 compute T3 formal
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Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. Results: This release of PMLB provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. Availability: PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

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