Overview of leakage scenarios in supervised machine learning
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 ma...
Saved in:
| Main Authors: | L. Sasse, E. Nicolaisen-Sobesky, J. Dukart, S. B. Eickhoff, M. Götz, S. Hamdan, V. Komeyer, A. Kulkarni, J. M. Lahnakoski, B. C. Love, F. Raimondo, Kaustubh R. Patil |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-05-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01193-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Rethinking Disclosure Prevention with Pointwise Maximal Leakage
by: Sara Saeidian, et al.
Published: (2025-03-01) -
Divergent Leakage Features of Anticyclonic and Cyclonic Mesoscale Eddies
by: Yanjiang Lin, et al.
Published: (2025-04-01) -
Leakage analysis of the recirculating cooling water system in petrochemical enterprises
by: GAO Xin, et al.
Published: (2025-01-01) -
Survey on Effective Disposal of E-Waste to Prevent Data Leakage
by: Akila Victor, et al.
Published: (2024-04-01) -
Automatic Distribution of PRVs for Leakage Reduction
by: Ramon Pérez, et al.
Published: (2024-09-01)