On Leakage in Machine Learning Pipelines

arXiv:2311.04179v2 Announce Type: replace
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 …

abstract applications arxiv cs.ai cs.lg fields healthcare machine machine learning marketing ml pipelines modeling performance physics pipelines prediction predictive predictive modeling sample tools type