Beyond Logistic Regression: Calibration With Dropouts In Tiny Neural Networks

Beyond Logistic Regression: Calibration With Dropouts In Tiny Neural Networks | 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 Beyond Logistic Regression: Calibration With Dropouts In Tiny Neural Networks Aaditya Kachhadiya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7001638/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Neural networks, such as Artificial Neural Networks (ANNs), often surpass simpler models in classification tasks but tend to exhibit overconfidence and poor calibration. This study examines the trade-offs between accuracy, calibration, and model complexity by comparing a compact dropout-regularized ANN with logistic regression on a real-world weather dataset containing approximately 145,000 samples. Reformulated as a three-class classification task for rainfall intensity prediction, the dataset is used to evaluate both models under two settings: the original feature space and a reduced feature space obtained via LinearDiscriminant Analysis (LDA). Evaluation metrics include classification accuracy, Expected Calibration Error (ECE), training time, etc. The ANN with dropout achieves the highest accuracy (82.57%) and best calibration (ECE = 0.0030), while logistic regression remains competitive despite its simplicity and smaller parameter footprint. LDA effectively reduces dimensionality from 16 to 2 with minimal performance loss, enabling faster training. These results highlight the utility of dropout for improving uncertainty calibration and emphasize the practicality of simple models in constrained environments, with additional significance drawn from the use of real-world data. Neural Network Calibration Dropout Tiny Neural Networks Logistic Regression Uncertainty Estimation Probabilistic Forecasting Model Confidence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-7001638","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479742504,"identity":"1871b3d5-38ae-4c7c-bb5f-acd7b745f923","order_by":0,"name":"Aaditya Kachhadiya","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Aaditya","middleName":"","lastName":"Kachhadiya","suffix":""}],"badges":[],"createdAt":"2025-06-29 08:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7001638/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7001638/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92644589,"identity":"dc796428-8607-44ec-974e-f59e0df4884e","added_by":"auto","created_at":"2025-10-02 09:32:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":824709,"visible":true,"origin":"","legend":"","description":"","filename":"aadityasubmissiontonplrevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7001638/v1_covered_6b016da9-5d53-48f3-bd94-469ae3b2d2bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Logistic Regression: Calibration With Dropouts In Tiny Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neural Network Calibration, Dropout, Tiny Neural Networks, Logistic Regression, Uncertainty Estimation, Probabilistic Forecasting, Model Confidence","lastPublishedDoi":"10.21203/rs.3.rs-7001638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7001638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Neural networks, such as Artificial Neural Networks (ANNs), often surpass simpler models in classification tasks but tend to exhibit overconfidence and poor calibration. 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