PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods | Zendy

Joseph D. Romano | Zendy; Trang T. Le | Zendy; William La Cava | Zendy; John Gregg | Zendy; Daniel J. Goldberg | Zendy; Praneel Chakraborty | Zendy; Natasha L. Ray | Zendy; Daniel Himmelstein | Zendy; Weixuan Fu | Zendy; Jason H. Moore | Zendy

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

Author(s) -

Joseph D. Romano,

Trang T. Le,

William La Cava,

John Gregg,

Daniel J. Goldberg,

Praneel Chakraborty,

Natasha L. Ray,

Daniel Himmelstein,

Weixuan Fu,

Jason H. Moore

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab727

Subject(s) - python (programming language) , benchmarking , computer science , benchmark (surveying) , open source , workflow , machine learning , data mining , source code , data collection , artificial intelligence , software , database , operating system , marketing , business , geodesy , geography , statistics , mathematics

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.

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