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arXiv · 2012.00058

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

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
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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. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

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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. 2020-11-30. PMLB v1.0: An open source dataset collection for benchmarking machine learning methods. https://arxiv.org/abs/2012.00058

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