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<oembed><version>1.0</version><provider_name>Glosarix</provider_name><provider_url>https://glosarix.com/en/</provider_url><author_name>Team Glosarix</author_name><author_url>https://glosarix.com/en/author/adm_glosarix/</author_url><title>Hyperparameter Estimation - Glosarix</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="RpY9I0ba0k"&gt;&lt;a href="https://glosarix.com/en/glossary/hyperparameter-estimation-en/"&gt;Hyperparameter Estimation&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://glosarix.com/en/glossary/hyperparameter-estimation-en/embed/#?secret=RpY9I0ba0k" width="600" height="338" title="&#x201C;Hyperparameter Estimation&#x201D; &#x2014; Glosarix" data-secret="RpY9I0ba0k" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>Description: Hyperparameter estimation is the process of determining the optimal values for hyperparameters in machine learning models. Hyperparameters are configurations set before the model training that influence its performance and generalization capability. Unlike model parameters, which are adjusted during training, hyperparameters must be selected in advance and can include elements such as learning rate, number [&hellip;]</description></oembed>
