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.
@article{arxiv.2311.04179,
title = {On Leakage in Machine Learning Pipelines},
author = {Leonard Sasse and Eliana Nicolaisen-Sobesky and Juergen Dukart and Simon B. Eickhoff and Michael Götz and Sami Hamdan and Vera Komeyer and Abhijit Kulkarni and Juha Lahnakoski and Bradley C. Love and Federico Raimondo and Kaustubh R. Patil},
journal= {arXiv preprint arXiv:2311.04179},
year = {2025}
}