Post-processing: Revision history

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18 March 2023

  • curprev 13:2613:26, 18 March 2023‎ Walle talk contribs‎ 3,809 bytes +3,809‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Post-processing, in the context of machine learning, refers to a set of techniques and methods applied to the output of a machine learning model in order to improve or refine its results. This may include steps such as data transformation, calibration, and thresholding. Post-processing is often used to enhance model performance, interpretability, and reliability when deployed in real-world applications. ==Purpose of P..."
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