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		<title>Imperfect but plentiful training data improves prediction of software user satisfaction</title>
		<link>https://bioengineer.org/imperfect-but-plentiful-training-data-improves-prediction-of-software-user-satisfaction/</link>
		
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		<pubDate>Mon, 26 Oct 2026 17:36:32 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[cross-validation]]></category>
		<category><![CDATA[data quality]]></category>
		<category><![CDATA[data selection]]></category>
		<category><![CDATA[ISBSG dataset]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[missing data imputation]]></category>
		<category><![CDATA[predictive modelling]]></category>
		<category><![CDATA[Software Engineering]]></category>
		<category><![CDATA[software projects]]></category>
		<category><![CDATA[support vector machines]]></category>
		<category><![CDATA[training data]]></category>
		<category><![CDATA[user satisfaction]]></category>
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					<description><![CDATA[A machine learning study using the ISBSG software project repository found that models trained on larger datasets containing low-quality records outperformed those trained on smaller, highly curated ones when predicting user satisfaction.]]></description>
		
		
		
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