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001-es BibID:	BIBFORM119746
035-os BibID:	(Cikkazonosító)127556
Első szerző:	Kovács György
Cím:	mlscorecheck: Testing the consistency of reported performance scores and experiments in machine learning / György Kovács, Attila Fazekas
Dátum:	2024
ISSN:	0925-2312
Megjegyzések:	Addressing the reproducibility crisis in artificial intelligence through the validation of reported experimental results is a challenging task. It necessitates either the reimplementation of techniques or a meticulous assessment of papers for deviations from the scientific method and best statistical practices. To facilitate the validation of reported results, we have developed numerical techniques capable of identifying inconsistencies between reported performance scores and various experimental setups in machine learning problems, including binary/multiclass classification and regression. These consistency tests are integrated into the open-source package mlscorecheck, which also provides specific test bundles designed to detect systematically recurring flaws in various fields, such as retina image processing and synthetic minority oversampling.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Binary classification Multiclass classification Regression Consistency testing Performance scores Open source
Megjelenés:	Neurocomputing. - 583 (2024), p. 1-4. -
További szerzők:	Fazekas Attila (1968-) (matematikus, informatikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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