Holdout Set

A holdout set is a subset of data withheld from model training to evaluate its performance on unseen data, crucial for assessing generalization and preventing overfitting. Current research emphasizes the ethical and practical implications of holdout set composition, particularly concerning fairness, bias mitigation in sensitive subgroups (e.g., in clinical prediction models), and efficient utilization in hyperparameter optimization and model updating. These studies highlight the importance of carefully designing holdout sets for reliable model evaluation and responsible deployment, impacting various fields from healthcare to natural language processing.