Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
For practitioners and researchers in supervised learning, this paper offers a structured guide to avoid common evaluation pitfalls, but it is largely a synthesis of known principles rather than a novel contribution.
This paper examines principles and pitfalls in evaluating supervised machine learning models, highlighting issues like the accuracy paradox, data leakage, and inappropriate metric selection through controlled experiments on benchmark datasets. It provides a structured foundation for selecting metrics and validation protocols to support robust evaluation.
The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread availability of machine learning libraries and automated workflows, model assessment is often reduced to the reporting of a small set of aggregate metrics, which can lead to misleading conclusions about real-world performance. This paper examines the principles, challenges, and practical considerations involved in evaluating supervised learning algorithms across classification and regression tasks. In particular, it discusses how evaluation outcomes are influenced by dataset characteristics, validation design, class imbalance, asymmetric error costs, and the choice of performance metrics. Through a series of controlled experimental scenarios using diverse benchmark datasets, the study highlights common pitfalls such as the accuracy paradox, data leakage, inappropriate metric selection, and overreliance on scalar summary measures. The paper also compares alternative validation strategies and emphasizes the importance of aligning model evaluation with the intended operational objective of the task. By presenting evaluation as a decision-oriented and context-dependent process, this work provides a structured foundation for selecting metrics and validation protocols that support statistically sound, robust, and trustworthy supervised machine learning systems.