Introduction: The Pitfalls of Overfitting and the Need for Robust Evaluation In the relentless pursuit of building accurate and reliable machine learning models, data scientists often focus solely on achieving the highest possible accuracy score on a held-out test set. However, this singular focus can be misleading. A model that performs exceptionally well on one
