Feature Data Split

Feature Data Split is a ml experimental control that separates examples for training, validation, and testing for input signals used by a machine learning model. 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 Feature Data Split when a feature distribution shifted, so the team could measure generalization honestly before the model moved into evaluation.”