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001043079 1001_ $$0P:(DE-Juel1)190306$$aSasse, L.$$b0
001043079 245__ $$aOverview of leakage scenarios in supervised machine learning
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001043079 520__ $$aMachine learning (ML) provides powerful tools for predictive modeling. ML’s popularity stems from the promise of sample-level prediction with applications across a variety of fields from physics and marketing to healthcare. However, if not properly implemented and evaluated, ML pipelines may contain leakage typically resulting in overoptimistic performance estimates and failure to generalize to new data. This can have severe negative financial and societal implications. Our aim is to expand understanding associated with causes leading to leakage when designing, implementing, and evaluating ML pipelines. Illustrated by concrete examples, we provide a comprehensive overview and discussion of various types of leakage that may arise in ML pipelines.
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001043079 7001_ $$0P:(DE-Juel1)180537$$aNicolaisen, Eliana$$b1$$ufzj
001043079 7001_ $$0P:(DE-Juel1)177727$$aDukart, Jürgen$$b2
001043079 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, S. B.$$b3
001043079 7001_ $$0P:(DE-HGF)0$$aGötz, M.$$b4
001043079 7001_ $$0P:(DE-HGF)0$$aHamdan, S.$$b5
001043079 7001_ $$0P:(DE-Juel1)187351$$aKomeyer, V.$$b6
001043079 7001_ $$0P:(DE-HGF)0$$aKulkarni, A.$$b7
001043079 7001_ $$0P:(DE-Juel1)179423$$aLahnakoski, J. M.$$b8
001043079 7001_ $$0P:(DE-HGF)0$$aLove, B. C.$$b9
001043079 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, F.$$b10
001043079 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b11$$eCorresponding author
001043079 773__ $$0PERI:(DE-600)2780218-8$$a10.1186/s40537-025-01193-8$$gVol. 12, no. 1, p. 135$$n1$$p135$$tJournal of Big Data$$v12$$x2196-1115$$y2025
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