Metric Hyperparameter Sweep

기계 지원 번역 초안 (Korean) for "Metric Hyperparameter Sweep": Metric Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for measurement of model behavior. It uses bounded search spaces, trial tracking, and early stopping so teams can find better configurations while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The machine learning team used Metric Hyperparameter Sweep when the metric changed after data cleanup, so the team could find better configurations before the model moved into evaluation.”