[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117849-en":3,"doc-seo-117849-105":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":4,"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},117849,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Interpretable machine learning for imbalanced credit scoring datasets","The class imbalance problem is common in credit scoring, where defaulters are far fewer than non-defaulters. Existing work has largely measured how imbalance harms predictive accuracy, while its impact on machine learning interpretability remains underexplored. This study examines how the stability of LIME and SHAP, two popular local interpretation methods, changes under progressively increasing imbalance. Experiments on UK residential mortgage data (2016–2020) show interpretations from both methods become less stable as imbalance grows, indicating adverse effects on interpretability. To verify robustness, the analysis on two open-source credit scoring datasets yields similar conclusions.","Edinburgh Research Explorer  \nInterpretable machine learning for imbalanced credit scoring datasets  \nCitation for published version:  \nChen, Y, Calabrese, R & Martin-Barragan, B 2023, ' Interpretable machine learning for imbalanced credit scoring datasets', European Journal of Operational Research. [https://doi.org/10.1016/j.ejor.2023.06.036](https://doi.org/10.1016/j.ejor.2023.06.036)  \nDigital Object Identifier (DOI):  \n10.1016/j.ejor.2023.06.036  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nEuropean Journal of Operational Research  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 30. Jul. 2023  \n ARTICLE IN PRESS   \nJID: EOR [m5G;July 3, 2023;20:3]  \nEuropean Journal of Operational Research xxx (xxxx) xxx  \nContents lists available at ScienceDirect  \nEuropean Journal of Operational Research  \njournal [homepage: www.elsevier.com/locate/ejor](homepage: www.elsevier.com/locate/ejor)  \n| Interfaces with Other Disciplines\u003Cbr>Interpretable machine learning for imbalanced credit scoring datasets Yujia Chen∗, Raffaella Calabrese, Belen Martin-Barragan\u003Cbr>Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh EH8 9JS, UK |  |  |\n| --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |\n| Article history:\u003Cbr>Received 1 August 2022\u003Cbr>Accepted 20 June 2023\u003Cbr>Available online xxx |  | The class imbalance problem is common in the credit scoring domain, as the number of defaulters is usually much less than the number of non-defaulters. To date, research on investigating the class imbalance problem has mainly focused on indicating and reducing the adverse effect of the class imbalance on the predictive accuracy of machine learning techniques, while the impact of that on machine learning interpretability has never been studied in the literature. This paper fills this gap by analysing how the stability of Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), two popular interpretation methods, are affected by class imbalance. Our experiments use 2016–2020 UK residential mortgage data collected from European Datawarehouse. We evaluate the stability of LIME and SHAP on datasets of progressively increased class imbalance. The results show that interpretations generated from LIME and SHAP are less stable as the class imbalance increases, which indicates that the class imbalance does have an adverse effect on machine learning interpretability. To check the robustness of our outcomes, we also analyse two open-source credit scoring datasets and we obtain similar results.\u003Cbr>© 2023 The Author(s). Published by Elsevier B.V.\u003Cbr>This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) |\n| Keywords:\u003Cbr>OR in banking Interpretability Stability\u003Cbr>Credit scoring Machine learning |  |  |\n\n1. Introduction  \nFinancial institutions rely on credit scoring models to estimate the default probability of borrowers and decide whether or not to approve loan applications. With the boosted enthusiasm in the machine learning-based predictive techniques adopted in finance, applications such as credit scoring have gained substantial interest from bo","cbCaijYo6eUdPXQR","https://ap.wps.com/l/cbCaijYo6eUdPXQR","pdf",4469207,1,17,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is class imbalance a concern in credit scoring?\",\"answer\":\"In credit scoring, defaulters are typically much fewer than non-defaulters. This imbalance can affect how models learn and influences downstream evaluation beyond accuracy.\"},{\"question\":\"How does this paper study interpretability under class imbalance?\",\"answer\":\"It analyzes how the stability of LIME and SHAP explanations changes as class imbalance increases. The paper evaluates stability on datasets with progressively increased imbalance.\"},{\"question\":\"What are the main findings regarding LIME and SHAP stability?\",\"answer\":\"Interpretations generated from LIME and SHAP become less stable as class imbalance increases. This suggests class imbalance adversely impacts machine learning interpretability.\"}]","Interpretable machine learning for imbalanced credit scoring datasets | PDF",1785679986,43,{"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},"interpretable-machine-learning-for-imbalanced-credit-scoring-datasets","",{"@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/interpretable-machine-learning-for-imbalanced-credit-scoring-datasets/117849/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is class imbalance a concern in credit scoring?","Question",{"text":75,"@type":76},"In credit scoring, defaulters are typically much fewer than non-defaulters. This imbalance can affect how models learn and influences downstream evaluation beyond accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does this paper study interpretability under class imbalance?",{"text":80,"@type":76},"It analyzes how the stability of LIME and SHAP explanations changes as class imbalance increases. The paper evaluates stability on datasets with progressively increased imbalance.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main findings regarding LIME and SHAP stability?",{"text":84,"@type":76},"Interpretations generated from LIME and SHAP become less stable as class imbalance increases. 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