{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pmlb-v1-0-an-open-source-dataset-collection","title":"PMLB v1.0: An open source dataset collection for benchmarking machine learning methods","arxiv_id":"2012.00058","date":"2020-11-30","proceeding":null,"authors":["Joseph D. Romano","Trang T. Le","William La Cava","John T. Gregg","Daniel J. Goldberg","Natasha L. Ray","Praneel Chakraborty","Daniel Himmelstein","Weixuan Fu","Jason H. Moore"],"abstract":"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. 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