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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>Label Distribution - Glosarix</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="Hm5D3h1gsq"&gt;&lt;a href="https://glosarix.com/en/glossary/label-distribution-en/"&gt;Label Distribution&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://glosarix.com/en/glossary/label-distribution-en/embed/#?secret=Hm5D3h1gsq" width="600" height="338" title="&#x201C;Label Distribution&#x201D; &#x2014; Glosarix" data-secret="Hm5D3h1gsq" 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: The label distribution in a dataset refers to how different classes or categories are represented within that dataset. This concept is crucial in the realm of machine learning, as an imbalance in label distribution can significantly affect the model&#x2019;s performance. For instance, if a dataset contains a large number of instances from one specific [&hellip;]</description></oembed>
