Journal Article

FZJ-2025-02765

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Overview of leakage scenarios in supervised machine learning

Sasse, L.FZJ* ; Nicolaisen, E.FZJ* ; Dukart, J.FZJ* ; Eickhoff, S. B.FZJ* ; Götz, M. ; Hamdan, S. ; Komeyer, V.FZJ* ; Kulkarni, A. ; Lahnakoski, J. M.FZJ* ; Love, B. C. ; Raimondo, F.FZJ* ; Patil, K. R. (Corresponding author)FZJ*

2025
SpringerOpen Heidelberg [u.a.]

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Please use a persistent id in citations: doi:10.1186/s40537-025-01193-8  doi:10.34734/FZJ-2025-02765

Abstract: Machine 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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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2025

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Database coverage:
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