Leakage (Data Science): Revision history

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    30 November 2024

    • curprev 19:2019:20, 30 November 2024‎ Prairie talk contribs‎ 5,267 bytes +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... Tag: Visual edit
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