  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.
