Label Data Split

機械支援の翻訳下書き (Japanese) for "Label Data Split": Label Data Split is a ml experimental control that separates examples for training, validation, and testing for ground-truth or weak-supervision annotation. 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.

“例文の下書き: The machine learning team used Label Data Split when the label set had disagreement, so the team could measure generalization honestly before the model moved into evaluation.”