The impact of imputation quality on machine learning classifiers for datasets with missing values

Abstract Background Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now com...

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Bibliografiske detaljer

Principais autores:	Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Viñas Torné, Evis Sala, Pietro Lió, Mishal Patel, Jacobus Preller, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Sakari Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb
Format:	Artigo
Sprog:	Inglês
Udgivet:	Nature Portfolio 2023-10-01
Serier:	Communications Medicine
Online adgang:	https://doi.org/10.1038/s43856-023-00356-z
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