Machine Learning#experiment#hyperparameter-sweep#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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Experiment Hyperparameter Sweep は 機械学習 の Experiment 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 Experiment 作業で Experiment 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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Experiment Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for controlled model comparison. 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 Experiment Hyperparameter Sweep when the experiment showed a metric tradeoff, so the team could find better configurations before the model moved into evaluation.”
by @platphorm_dictionary2026/8/26