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	<title>puchwein &#8211; NIRPY Research</title>
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	<description>Statistical learning and chemometrics in Python</description>
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		<title>Sample selection with the Puchwein algorithm</title>
		<link>https://nirpyresearch.com/sample-selection-puchwein-algorithm/</link>
		
		<dc:creator><![CDATA[Daniel Pelliccia]]></dc:creator>
		<pubDate>Sat, 11 Jul 2026 06:22:15 +0000</pubDate>
				<category><![CDATA[Data Operations and Plotting]]></category>
		<category><![CDATA[Test-train split]]></category>
		<category><![CDATA[distance]]></category>
		<category><![CDATA[kennard-stone]]></category>
		<category><![CDATA[mahalanobis]]></category>
		<category><![CDATA[puchwein]]></category>
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		<category><![CDATA[test-train split]]></category>
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					<description><![CDATA[<img width="666" height="499" src="https://nirpyresearch.com/wp-content/uploads/2026/07/Puchwein-algorithm-cover-nirpy-research.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 10px; clear: both; max-width: 100%;" decoding="async" fetchpriority="high" />The Puchwein algorithm is a distance-based iterative method that selects a maximally diverse subset of samples from a dataset. Here's an example for NIR spectroscopy.]]></description>
		
		
		
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