Metric Data Split

Metric Data Split is a ml experimental control that separates examples for training, validation, and testing for measurement of model behavior. It uses randomization rules, leakage checks, and seed tracking so teams can measure generalization honestly while keeping evidence, reliability, and public-safe operational boundaries clear.

“The machine learning team used Metric Data Split when the metric changed after data cleanup, so the team could measure generalization honestly before the model moved into evaluation.”