PipeBench: a benchmarking framework for end-to-end machine learning pipelines.

Agal S, Katariya R

Open source

DOI: 10.1038/s41598-026-53722-x
Published: 2026 May 28
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-53722-x,
  title = {PipeBench: a benchmarking framework for end-to-end machine learning pipelines.},
  author = {Agal S and Katariya R},
  year = {2026},
  journal = {Scientific reports},
  doi = {10.1038/s41598-026-53722-x},
  url = {https://doi.org/10.1038/s41598-026-53722-x}
}

RIS

TY  - JOUR
TI  - PipeBench: a benchmarking framework for end-to-end machine learning pipelines.
AU  - Agal S
AU  - Katariya R
PY  - 2026
JO  - Scientific reports
DO  - 10.1038/s41598-026-53722-x
UR  - https://doi.org/10.1038/s41598-026-53722-x
ER  -

APA

S, A., & R, K. (2026). PipeBench: a benchmarking framework for end-to-end machine learning pipelines.. Scientific reports. https://doi.org/10.1038/s41598-026-53722-x

Source records

pubmed · retrieved 2026-09-25T23:57:20.603Z
europe-pmc · retrieved 2026-09-25T23:57:20.622Z
doaj · retrieved 2026-09-25T23:57:20.615Z