Tag: Benchmark Contamination

In traditional machine learning engineering, validating a model’s generalization capability relies on keeping test sets strictly isolated from training corpora. When an algorithm is trained on a dataset, evaluators hold back a pristine, unseen test partition to measure true out-of-sample accuracy. If a model performs well on this held-out data, engineers gain empirical confidence that […]