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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Chris Meek</author_name><author_url>https://www.microsoft.com/en-us/research/people/meek/</author_url><title>A Characterization of Prediction Errors - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="h8jE0NTnl0"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/characterization-prediction-errors/"&gt;A Characterization of Prediction Errors&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/characterization-prediction-errors/embed/#?secret=h8jE0NTnl0" width="600" height="338" title="&#x201C;A Characterization of Prediction Errors&#x201D; &#x2014; Microsoft Research" data-secret="h8jE0NTnl0" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of prediction errors (mislabeling, representation, learner and boundary errors) and demonstrate that these four types characterize all prediction errors. In addition, we describe potential remedies and tools that can be used to [&hellip;]</description></oembed>
