Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning
It addresses data leakage risks for practitioners using ML tools without deep expertise, which is an incremental analysis of a known issue.
This paper tackles the problem of data leakage in machine learning, where unintended information contaminates training data, leading to overly optimistic performance estimates that fail in real-world scenarios. It categorizes data leakage, explores its occurrence in transfer learning, and compares inductive and transductive ML frameworks to highlight risks for practitioners.
Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, with the increasing accessibility of ML tools, many practitioners, lacking deep ML expertise, adopt a "push the button" approach, utilizing user-friendly interfaces without a thorough understanding of underlying algorithms. While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation. This paper addresses a critical issue in ML, known as data leakage, where unintended information contaminates the training data, impacting model performance evaluation. Users, due to a lack of understanding, may inadvertently overlook crucial steps, leading to optimistic performance estimates that may not hold in real-world scenarios. The discrepancy between evaluated and actual performance on new data is a significant 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 specific task being addressed, investigates its occurrence in Transfer Learning, and compares standard inductive ML with transductive ML frameworks. The conclusion summarizes key findings, emphasizing the importance of addressing data leakage for robust and reliable ML applications.