Label Calibration Curve

機械支援の翻訳下書き (Japanese) for "Label Calibration Curve": Label Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for ground-truth or weak-supervision annotation. It uses bucketed predictions, reliability diagrams, and threshold analysis so teams can make confidence scores useful while keeping evidence, reliability, and public-safe operational boundaries clear.

“例文の下書き: The machine learning team used Label Calibration Curve when the label set had disagreement, so the team could make confidence scores useful before the model moved into evaluation.”