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Machine Learning (ML) has revolutionized various domains, offering predictivecapabilities in several areas. However, with the increasing accessibility of MLtools, many practitioners, lacking deep ML expertise, adopt a "push the button"approach, utilizing user-friendly interfaces without a thorough understandingof underlying algorithms. While this approach provides convenience, it raisesconcerns about the reliability of outcomes, leading to challenges such asincorrect performance evaluation. This paper addresses a critical issue in ML,known as data leakage, where unintended information contaminates the trainingdata, impacting model performance evaluation. Users, due to a lack ofunderstanding, may inadvertently overlook crucial steps, leading to optimisticperformance estimates that may not hold in real-world scenarios. Thediscrepancy between evaluated and actual performance on new data is asignificant concern. In particular, this paper categorizes data leakage in ML,discussing how certain conditions can propagate through the ML workflow.Furthermore, it explores the connection between data leakage and the specifictask being addressed, investigates its occurrence in Transfer Learning, andcompares standard inductive ML with transductive ML frameworks. The conclusionsummarizes key findings, emphasizing the importance of addressing data leakagefor robust and reliable ML applications.

Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning