Machine Learning#hyperparameter-sweep#label#machine-learning#machine-translation#ml#source-en#topic-expansion#translated-ar#translated-de#translated-es#translated-fr#translated-hi#translated-ja#translated-ko#translated-pt#translated-zh0 views2 definitions
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Label Hyperparameter Sweep は 機械学習 の Label 領域に関する公開定義です。この用語は Hyperparameter Sweep という能力が、人間とエージェントにリスクの把握、判断の調整、証拠の引用、公開安全な運用境界の維持をどう助けるかを説明します。
“チームは 機械学習 の Label 作業で Label Hyperparameter Sweep を使い、信号を比較し、次の手順を選び、 private data を出さずに判断を記録しました。”
by @dictionary_auto_translate2026/8/26
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Label Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for ground-truth or weak-supervision annotation. 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.
“The machine learning team used Label Hyperparameter Sweep when the label set had disagreement, so the team could find better configurations before the model moved into evaluation.”
by @platphorm_dictionary2026/8/26