Bias-Variance Tradeoff Decomposition based Machine Learning Model Selection: Application to Credit Risk Analysis.

Bias-Variance Tradeoff Decomposition based Machine Learning Model Selection: Application to Credit Risk Analysis. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bias-Variance Tradeoff Decomposition based Machine Learning Model Selection: Application to Credit Risk Analysis. Jaber Jemai, Ali Daud This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6442602/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Aug, 2025 Read the published version in International Journal of Data Science and Analytics → Version 1 posted 11 You are reading this latest preprint version Abstract In Machine Learning (ML), model selection is crucial since it consists of choosing the model to use in operations. The selected model is expected to exhibit optimal performance when run on unseen data. In this paper, we define a framework where we propose a new strategy for model selection based on the decomposition of the bias-variance tradeoff. The framework is defined by three dimensions: the model complexity, the learning set size, and the loss level. Our technique initially addresses the sample size dimension by constructing a learning convergence detection mechanism. After determining the optimal sample size, the ideal model complexity level is chosen by quantifying the bias-variance tradeoff. We used this method to find the optimal XGBoost model to address the credit risk classification problem. The study revealed that the learning process converges to a steady state at a certain size of the training set, where no significant reduction in the loss function can be seen. Furthermore, increasing the level of complexity of the model (maximum depth in this study) does not significantly improve the performance of the model. Moreover, the correlation and covariance of the bias and the variance at the optimal complexity level are the lowest among all other models. It's worth noting that the performance of the selected model was the best during the test phase. Model Selection Bias-Variance Tradeoff Decomposition Learning Convergence Financial Technology Credit Risk Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Aug, 2025 Read the published version in International Journal of Data Science and Analytics → Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviews received at journal 28 May, 2025 Reviews received at journal 27 May, 2025 Reviews received at journal 20 May, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 14 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6442602","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450946973,"identity":"8f6702d3-81a7-42ec-968f-9caa82ea508c","order_by":0,"name":"Jaber Jemai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACxoYDQNLAJoFkLWkJDGwkWnaYBC3MjYePbvhRcD6Pf34D44cfDHZyRDjsWNrNHoPbxRLHGJglexiSjYnQcsbsBo/B7cQNQIdJA21NbCCs5fy3m38MzoG0MP9mYKivJ0LLGbbbPAYHQFrYpMHhQIRfzG7LGCQnzjiW2GbZY3DckKAthjMOP7v55o9dYn/z4cM3flRUyxO0xXDGAYSFwDglqIGBQZ6foENGwSgYBaNgxAMA17g++8h7u+4AAAAASUVORK5CYII=","orcid":"","institution":"Higher Colleges of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jaber","middleName":"","lastName":"Jemai","suffix":""},{"id":450946974,"identity":"04d5a658-a0b5-4d7a-9afb-e0039e8b6355","order_by":1,"name":"Ali Daud","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Daud","suffix":""}],"badges":[],"createdAt":"2025-04-14 05:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6442602/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6442602/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41060-025-00864-2","type":"published","date":"2025-08-11T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89310638,"identity":"3741cf39-3dc7-463f-a83c-e12cb2decd9c","added_by":"auto","created_at":"2025-08-18 16:09:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":615894,"visible":true,"origin":"","legend":"","description":"","filename":"Biasvariancedecomopositionformodelselection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6442602/v1_covered_8f8aa6fb-8229-4212-a942-2edec625cead.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bias-Variance Tradeoff Decomposition based Machine Learning Model Selection: Application to Credit Risk Analysis.","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and Analytics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Model Selection, Bias-Variance Tradeoff Decomposition, Learning Convergence, Financial Technology, Credit Risk Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6442602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6442602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In Machine Learning (ML), model selection is crucial since it consists of choosing the model to use in operations. 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