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

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 complete sa...

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Detalles Bibliográficos
Autores principales: Shadbahr, Tolou, Roberts, Michael, Stanczuk, Jan, Gilbey, Julian, Teare, Philip, Dittmer, Sören, Thorpe, Matthew, Torné, Ramon Viñas, Sala, Evis, Lió, Pietro, Patel, Mishal, Preller, Jacobus, Rudd, James H. F., Mirtti, Tuomas, Rannikko, Antti Sakari, Aston, John A. D., Tang, Jing, Schönlieb, Carola-Bibiane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558448/
https://www.ncbi.nlm.nih.gov/pubmed/37803172
http://dx.doi.org/10.1038/s43856-023-00356-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558448/
https://www.ncbi.nlm.nih.gov/pubmed/37803172
http://dx.doi.org/10.1038/s43856-023-00356-z

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