Leakage (Data Science): 편집 역사

2024년 12월 1일 (일)

• 최신이전 09:202024년 12월 1일 (일) 09:20‎ Prairie 토론 기여‎ 5,267 바이트 +5,267‎ 새 문서: '''Leakage''' in data science refers to a situation where information from outside the training dataset is inappropriately used to build or evaluate a model. This results in overoptimistic performance metrics during model evaluation, as the model effectively "cheats" by having access to information it would not have in a real-world application. Leakage is a critical issue in machine learning workflows and can lead to misleading conclusions and poor model generalization. ==Types... 태그: 시각 편집