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		<title>Metrics for regression and classification &#8211; quick note</title>
		<link>https://petaminds.com/metrics-for-regression-and-classification-quick-note/</link>
					<comments>https://petaminds.com/metrics-for-regression-and-classification-quick-note/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Tue, 18 Feb 2020 04:10:33 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[accuracy]]></category>
		<category><![CDATA[mae]]></category>
		<category><![CDATA[metrics]]></category>
		<category><![CDATA[precision]]></category>
		<category><![CDATA[recall]]></category>
		<category><![CDATA[rmse]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2214</guid>

					<description><![CDATA[<p>In this article, we review some common metrics and their uses for two main ML problems, i.e. regression and classification. Regression Metrics Most of the blogs have focussed on classification metrics like precision, recall, AUC etc. For a change, I wanted to explore all kinds of metrics including those used in regression as well. MAE [&#8230;]</p>
<p>The post <a href="https://petaminds.com/metrics-for-regression-and-classification-quick-note/">Metrics for regression and classification &#8211; quick note</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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