Holdout (Data Science): Revision history

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    30 November 2024

    • curprev 23:3823:38, 30 November 2024‎ Fortify talk contribs‎ 3,203 bytes +3,203‎ 새 문서: '''Holdout''' in data science refers to a method used to evaluate the performance of machine learning models by splitting the dataset into separate parts, typically a training set and a testing set. The testing set, often called the "holdout set," is kept aside during model training and is only used for final evaluation to ensure unbiased performance metrics. ==How Holdout Works== The holdout method involves the following steps: *The dataset is split into two (or sometimes three... Tag: Visual edit
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