Classification of datasets with imputed missing values

dc.contributor.affiliationResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland - Shadbahr, Tolou
dc.contributor.affiliationDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK - Roberts, Michael
dc.contributor.affiliationDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK - Stanczuk, Jan
dc.contributor.affiliationDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK - Gilbey, Julian
dc.contributor.affiliationAstraZeneca, Cambridge, UK - Teare, Philip
dc.contributor.authorShadbahr, Tolou
dc.contributor.authorRoberts, Michael
dc.contributor.authorStanczuk, Jan
dc.contributor.authorGilbey, Julian
dc.contributor.authorTeare, Philip
dc.date.accessioned2025-03-24T15:23:15Z
dc.date.issued2023-08-10
dc.date.issued2023-08-10
dc.descriptionThis repository has data and scripts to perform imputation on datasets with missing data, and then to classify the resulting imputed datasets. It also contains the scripts necessary to reproduce all figures in the paper. This repository forms part of the supplementary material for the paper: Shadbahr, T. and Roberts, M. and Stanczuk, J. and Gilbey, J. and Teare, P. et al., "The Impact of Imputation Quality on Machine Learning Classifiers for Datasets with Missing Values".
dc.identifierhttps://doi.org/10.5281/zenodo.8234032
dc.identifier.urihttps://hydatakatalogi-test-24.it.helsinki.fi/handle/123456789/11051
dc.rightsOpen
dc.rights.licensecc-by-4.0
dc.subjectimputation
dc.subjectmissing data
dc.subjectmachine learning
dc.subjectclassification
dc.titleClassification of datasets with imputed missing values
dc.typesoftware
dc.typesoftware
Zenodo


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