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	<title>Non-additive machine learning models &#8211; BIOENGINEER.ORG</title>
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		<title>Revolutionizing Interaction Discovery in Machine Learning</title>
		<link>https://bioengineer.org/revolutionizing-interaction-discovery-in-machine-learning/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sat, 11 Oct 2025 23:00:15 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Error-controlled interaction discovery]]></category>
		<category><![CDATA[High-dimensional data analysis]]></category>
		<category><![CDATA[Model interpretability frameworks]]></category>
		<category><![CDATA[Non-additive machine learning models]]></category>
		<category><![CDATA[Predictive modeling reliability]]></category>
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					<description><![CDATA[In the vast landscape of machine learning, a groundbreaking study by Chen, Jiang, and Noble sheds light on the intricate problem of non-additive interactions within predictive models. The fundamental premise lies in the notion that the effect of an input on the model’s output isn’t merely the sum of its individual contributions. Instead, interactions between [&#8230;]]]></description>
		
		
		
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