🤖 AI Summary
This work systematically exposes data leakage risks in machine learning and transfer learning arising from user operational errors—risks that severely distort model evaluation, affecting over 70% of “off-the-shelf” practices. To address this, we propose the first multidimensional leakage taxonomy, explicitly grounded in task type, learning paradigm (inductive vs. transductive), and transfer scenario; it identifies six high-frequency leakage patterns. Through workflow modeling, empirical case studies, and comparative analysis across learning paradigms, we characterize the mechanisms of illicit forward propagation of information during preprocessing, validation, and fine-tuning stages. Innovatively, we develop a leakage-prevention checklist and standardized procedural guidelines—delivering a practical, actionable methodology for robust and reliable model evaluation.
📝 Abstract
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.