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	<title>Classification pipelines &#8211; NIRPY Research</title>
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	<description>Statistical learning and chemometrics in Python</description>
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		<title>Multi-class classification for NIR spectroscopy: the very basics</title>
		<link>https://nirpyresearch.com/multi-class-classification-nir-spectroscopy/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Sat, 08 Jul 2023 10:03:50 +0000</pubDate>
				<category><![CDATA[Classification]]></category>
		<category><![CDATA[Classification metrics]]></category>
		<category><![CDATA[Classification pipelines]]></category>
		<category><![CDATA[Logistic Regression]]></category>
		<category><![CDATA[Principal Components Analysis]]></category>
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					<description><![CDATA[<img width="666" height="499" src="https://nirpyresearch.com/wp-content/uploads/2023/07/multi-class-classification.jpeg" class="webfeedsFeaturedVisual wp-post-image" alt="Multi-class classification nirpy research" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" fetchpriority="high" />Multi-class classification aims and subdividing samples into one of multiple predefined categories. In this post we explore a basic classifier and discuss important metrics such as accuracy and AUC score. ]]></description>
		
		
		
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