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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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
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
Descripción
Sumario: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.