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PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods
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 wit...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756190/ https://www.ncbi.nlm.nih.gov/pubmed/34677586 http://dx.doi.org/10.1093/bioinformatics/btab727 |
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author | Romano, Joseph D Le, Trang T La Cava, William Gregg, John T Goldberg, Daniel J Chakraborty, Praneel Ray, Natasha L Himmelstein, Daniel Fu, Weixuan Moore, Jason H |
author_facet | Romano, Joseph D Le, Trang T La Cava, William Gregg, John T Goldberg, Daniel J Chakraborty, Praneel Ray, Natasha L Himmelstein, Daniel Fu, Weixuan Moore, Jason H |
author_sort | Romano, Joseph D |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8756190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-87561902022-01-13 PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods Romano, Joseph D Le, Trang T La Cava, William Gregg, John T Goldberg, Daniel J Chakraborty, Praneel Ray, Natasha L Himmelstein, Daniel Fu, Weixuan Moore, Jason H Bioinformatics Applications Notes 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. Oxford University Press 2021-10-22 /pmc/articles/PMC8756190/ /pubmed/34677586 http://dx.doi.org/10.1093/bioinformatics/btab727 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Romano, Joseph D Le, Trang T La Cava, William Gregg, John T Goldberg, Daniel J Chakraborty, Praneel Ray, Natasha L Himmelstein, Daniel Fu, Weixuan Moore, Jason H PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods |
title | PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods |
title_full | PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods |
title_fullStr | PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods |
title_full_unstemmed | PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods |
title_short | PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods |
title_sort | pmlb v1.0: an open-source dataset collection for benchmarking machine learning methods |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756190/ https://www.ncbi.nlm.nih.gov/pubmed/34677586 http://dx.doi.org/10.1093/bioinformatics/btab727 |
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