[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125710-en":3,"doc-seo-125710-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125710,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The impact of imputation quality on machine learning classifiers for datasets with missing values","Machine learning classification on real-world data is often hindered by missing feature values, which are usually filled via imputation before training and evaluation. This work studies how classifier choice and imputation strategy jointly affect performance and whether the imputed data matches the underlying distribution. Using three simulated and three clinical datasets, the analysis applies ANOVA, compares standard imputation-quality assessments, and introduces sliced-Wasserstein-based discrepancy scores. Results show test missingness most strongly degrades accuracy, while poor imputation harms interpretability.","ARTICLE  \n [https://doi.org/10.1038/s43856-023-00356-z](https://doi.org/10.1038/s43856-023-00356-z)  OPEN  \nThe impact of imputation quality on machine  \nlearning classifiers for datasets with missing values  \nTolou Shadbahr  1,23, Michael Roberts  2,3,23✉ , Jan Stanczuk2,23, Julian Gilbey  2,23, Philip Teare3,23, Sören Dittmer2,4, Matthew Thorpe5, Ramon Viñas Torné6, Evis Sala  7, Pietro Lió  5, Mishal Patel3,8, Jacobus Preller  9, AIX-COVNET Collaboration*, James H. F. Rudd  10, Tuomas Mirtti  1,11,12, Antti Sakari Rannikko1,12,13, John A. D. Aston14, Jing Tang  1 & Carola-Bibiane Schönlieb2  \nAbstract  \nBackground 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 samples. The focus of the machine learning researcher is tooptimise the classifier’s performance.  \nMethods We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data.  \nResults The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised.  \nConclusions It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.  \nPlain language summary  \nMany artificial intelligence (AI) methods aim to classify samples of data into groups, e.g., patients with disease vs. those without. This often requires datasets to be complete, i. e., that all data has been collected for all samples. However, in clinical practice this is often not the case and some data can be missing. One solution is to ‘complete’ the dataset using a technique called imputation to replace those missing values. However, assessing how well the imputation method performs is challenging. In this work, we demonstrate why people should care about imputation, develop a new method for assessing imputation quality, and demonstrate that if we build AI models on poorly imputed data, the model can give different results to those we would hope for. Our findings may improve the utility and quality of AI models in the clinic.  \n1 Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 2 Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK. 3 Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK. 4 ZeTeM, University of Bremen, Bremen, Germany. 5 Department of Mathematics, University of Manchester, Manchester, UK. 6 Department of Computer Science and Technology, University of Cambridge, Cambridge, UK. 7 Department of Radiology, University of Cambridge, Cambridge, UK. 8 Clinical Pharmacology & Safety Sciences, AstraZeneca, Cambridge, UK. 9 Addenbrooke’s Hospital, Cambridge University Hospitals NHS Trust, Cambridge, UK. 10 Department of M","cbCaitWFLK65cPXF","https://ap.wps.com/l/cbCaitWFLK65cPXF","pdf",2359384,1,15,"English","en",105,"# Abstract\n## Methods\n## Results\n## Conclusions\n# Plain language summary\n# Introduction","[{\"question\":\"Why does imputation quality matter for downstream machine learning classification?\",\"answer\":\"Because classifier performance and model interpretability can degrade when imputed data does not reflect the true underlying distribution. The study shows substantial accuracy declines as test missingness increases, especially under poorly aligned imputations.\"},{\"question\":\"What datasets and evaluation setup are used in the study?\",\"answer\":\"Three simulated and three real-world clinical datasets are used, spanning different feature types and missingness patterns. The work first evaluates how downstream classification depends on both classifier and imputation methods.\"},{\"question\":\"How are imputation-quality assessment methods evaluated and what new method is introduced?\",\"answer\":\"Commonly used imputation-quality measures are compared against a new class of discrepancy scores based on sliced Wasserstein distance. The new scores better reflect alignment with the underlying data distribution.\"}]","The impact of imputation quality on machine learning classifiers for datasets with missing values | PDF",1785900779,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-impact-of-imputation-quality-on-machine-learning-classifiers-for-datasets-with-missing-values","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-impact-of-imputation-quality-on-machine-learning-classifiers-for-datasets-with-missing-values/125710/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does imputation quality matter for downstream machine learning classification?","Question",{"text":75,"@type":76},"Because classifier performance and model interpretability can degrade when imputed data does not reflect the true underlying distribution. The study shows substantial accuracy declines as test missingness increases, especially under poorly aligned imputations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets and evaluation setup are used in the study?",{"text":80,"@type":76},"Three simulated and three real-world clinical datasets are used, spanning different feature types and missingness patterns. The work first evaluates how downstream classification depends on both classifier and imputation methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How are imputation-quality assessment methods evaluated and what new method is introduced?",{"text":84,"@type":76},"Commonly used imputation-quality measures are compared against a new class of discrepancy scores based on sliced Wasserstein distance. 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