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    <title>Machine learning glossary</title>
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    <pubDate>Thu, 09 Dec 2021 23:46:09 &#43;0800</pubDate>
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    <description><![CDATA[A A/B testing A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures.
accuracy The fraction of predictions that a classification model got right.]]></description>
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