Don't push the button! Exploring data leakage risks in machine learning and transfer learning.
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| Title: | Don't push the button! Exploring data leakage risks in machine learning and transfer learning. |
|---|---|
| Authors: | Apicella, Andrea1 (AUTHOR) andapicella@unisa.it, Isgrò, Francesco2 (AUTHOR) francesco.isgro@unina.it, Prevete, Roberto2 (AUTHOR) roberto.prevete@unina.it |
| Source: | Artificial Intelligence Review. Nov2025, Vol. 58 Issue 11, p1-58. 58p. |
| Subjects: | Machine learning, Transfer of training, Evaluation methodology, Supervised learning, Data security failures |
| Abstract: | Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, there is growing evidence in the literature that ML approaches are not always used appropriately, leading to incorrect and sometimes overly optimistic results. One reason for this inappropriate use of ML may be the increasing availability of machine learning tools, leading to what we call the "push the button" approach. While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation. In particular, this paper addresses a critical issue in ML, known as data leakage, where unintended information contaminates the training data, impacting model performance evaluation. Indeed, crucial steps in ML pipeline can be inadvertently overlooked, leading to optimistic performance estimates that may not hold in real-world scenarios. The discrepancy between evaluated and actual performance on new data is a significant concern. In particular, this paper categorizes data leakage in ML, discussing how certain conditions can propagate through the ML approach workflow. Furthermore, it explores the connection between data leakage and the specific task being addressed, investigates its occurrence in Transfer Learning framework, and compares standard inductive ML with transductive ML paradigms. The conclusion summarizes key findings, emphasizing the importance of addressing data leakage for robust and reliable ML applications considering tasks and generalization goals. [ABSTRACT FROM AUTHOR] |
| Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187436280 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Don't push the button! Exploring data leakage risks in machine learning and transfer learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Apicella%2C+Andrea%22">Apicella, Andrea</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> andapicella@unisa.it</i><br /><searchLink fieldCode="AR" term="%22Isgrò%2C+Francesco%22">Isgrò, Francesco</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> francesco.isgro@unina.it</i><br /><searchLink fieldCode="AR" term="%22Prevete%2C+Roberto%22">Prevete, Roberto</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> roberto.prevete@unina.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>. Nov2025, Vol. 58 Issue 11, p1-58. 58p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Transfer+of+training%22">Transfer of training</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+security+failures%22">Data security failures</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, there is growing evidence in the literature that ML approaches are not always used appropriately, leading to incorrect and sometimes overly optimistic results. One reason for this inappropriate use of ML may be the increasing availability of machine learning tools, leading to what we call the "push the button" approach. While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation. In particular, this paper addresses a critical issue in ML, known as data leakage, where unintended information contaminates the training data, impacting model performance evaluation. Indeed, crucial steps in ML pipeline can be inadvertently overlooked, leading to optimistic performance estimates that may not hold in real-world scenarios. The discrepancy between evaluated and actual performance on new data is a significant concern. In particular, this paper categorizes data leakage in ML, discussing how certain conditions can propagate through the ML approach workflow. Furthermore, it explores the connection between data leakage and the specific task being addressed, investigates its occurrence in Transfer Learning framework, and compares standard inductive ML with transductive ML paradigms. The conclusion summarizes key findings, emphasizing the importance of addressing data leakage for robust and reliable ML applications considering tasks and generalization goals. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187436280 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10462-025-11326-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 58 StartPage: 1 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Transfer of training Type: general – SubjectFull: Evaluation methodology Type: general – SubjectFull: Supervised learning Type: general – SubjectFull: Data security failures Type: general Titles: – TitleFull: Don't push the button! Exploring data leakage risks in machine learning and transfer learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Apicella, Andrea – PersonEntity: Name: NameFull: Isgrò, Francesco – PersonEntity: Name: NameFull: Prevete, Roberto IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 58 – Type: issue Value: 11 Titles: – TitleFull: Artificial Intelligence Review Type: main |
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