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.
@article{arxiv.2012.00058,
title = {PMLB v1.0: An open source dataset collection for benchmarking machine learning methods},
author = {Joseph D. Romano and Trang T. Le and William La Cava and John T. Gregg and Daniel J. Goldberg and Natasha L. Ray and Praneel Chakraborty and Daniel Himmelstein and Weixuan Fu and Jason H. Moore},
journal= {arXiv preprint arXiv:2012.00058},
year = {2021}
}
Comments
4 pages, 1 figure. *: These authors contributed equally