- author = "Joseph D. Romano and Trang T. Le and William {La Cava} and John T. Gregg and Daniel J. Goldberg and Praneel Chakraborty and Natasha L. Ray and Daniel Himmelstein and Weixuan Fu and Jason H. Moore",
- title = "{PMLB} v1.0: an open-source dataset collection for benchmarking machine learning methods",
- journal = "Bioinformatics",
- year = "2022",
- volume = "38",
- number = "3",
- pages = "878--880",
- month = jan # " 12",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://pubmed.ncbi.nlm.nih.gov/34677586/",
-
DOI = "
10.1093/bioinformatics/btab727",
-
code_url = "https://github.com/EpistasisLab/pmlb",
-
abstract = "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 (Penn Machine Learning Benchmarks) 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 and implementation: 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.",
-
notes = "PMID: 34677586 PMCID: PMC8756190
See also arXiv preprint arXiv:2012.00058",
