Home > Publications database > Julearn: an easy-touse library for leakage-free evaluation and inspection of ML models > print

001     1023472
005     20250204113806.0
024 7 _ |a 10.46471/gigabyte.113
|2 doi
024 7 _ |a 10.34734/FZJ-2024-01705
|2 datacite_doi
037 _ _ |a FZJ-2024-01705
100 1 _ |a Hamdan, Sami
|0 P:(DE-Juel1)184874
|b 0
|u fzj
245 _ _ |a Julearn: an easy-touse library for leakage-free evaluation and inspection of ML models
260 _ _ |a [Erscheinungsort nicht ermittelbar]
|c 2024
|b GigaScience Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1734072519_20227
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a This work was partly supported by the Helmholtz-AI project DeGen (ZT-I-PF-5-078), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project PA 3634/1-1 and project-ID 431549029–SFB 1451 project B05, the Helmholtz Imaging Platform and eBRAIN Health (HORIZON-INFRA-2021-TECH-01).
520 _ _ |a The fast-paced development of machine learning (ML) and its increasing adoption in research challenge researchers without extensive training in ML. In neuroscience, ML can help understand brain-behavior relationships, diagnose diseases and develop biomarkers using data from sources like magnetic resonance imaging and electroencephalography. Primarily, ML builds models to make accurate predictions on unseen data. Researchers evaluate models' performance and generalizability using techniques such as cross-validation (CV). However, choosing a CV scheme and evaluating an ML pipeline is challenging and, if done improperly, can lead to overestimated results and incorrect interpretations. Here, we created julearn, an open-source Python library allowing researchers to design and evaluate complex ML pipelines without encountering common pitfalls. We present the rationale behind julearn’s design, its core features, and showcase three examples of previously-published research projects. Julearn simplifies the access to ML providing an easy-to-use environment. With its design, unique features, simple interface, and practical documentation, it poses as a useful Python-based library for research projects.
536 _ _ |a 5253 - Neuroimaging (POF4-525)
|0 G:(DE-HGF)POF4-5253
|c POF4-525
|f POF IV
|x 0
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
|0 G:(DE-HGF)POF4-5251
|c POF4-525
|f POF IV
|x 1
536 _ _ |a SFB 1451 B05 - Einzelfallvorhersagen der motorischen Fähigkeiten bei Gesunden und Patienten mit motorischen Störungen (B05) (458640473)
|0 G:(GEPRIS)458640473
|c 458640473
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a More, Shammi
|0 P:(DE-Juel1)177823
|b 1
700 1 _ |a Sasse, Leonard
|0 P:(DE-Juel1)190306
|b 2
|u fzj
700 1 _ |a Komeyer, Vera
|0 P:(DE-Juel1)187351
|b 3
|u fzj
700 1 _ |a Patil, Kaustubh
|0 P:(DE-Juel1)172843
|b 4
|u fzj
700 1 _ |a Raimondo, Federico
|0 P:(DE-Juel1)185083
|b 5
|e Corresponding author
773 _ _ |a 10.46471/gigabyte.113
|g Vol. 2024, p. 1 - 16
|0 PERI:(DE-600)3072630-X
|p
|t GigaByte
|v
|y 2024
|x 2709-4715
856 4 _ |u https://juser.fz-juelich.de/record/1023472/files/6bj1gFQZ.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1023472
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)184874
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)190306
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)187351
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)172843
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)185083
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5253
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5251
|x 1
914 1 _ |y 2024
915 p c |a APC keys set
|0 PC:(DE-HGF)0000
|2 APC
915 p c |a DOAJ Journal
|0 PC:(DE-HGF)0003
|2 APC
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-08-23
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-08-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-03-18T08:47:19Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-03-18T08:47:19Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Open peer review
|d 2024-03-18T08:47:19Z
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
|k INM-7
|l Gehirn & Verhalten
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21