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    <title>Calibration on Nalar</title>
    <link>https://nalar.dev/tags/calibration/</link>
    <description>Recent content in Calibration on Nalar</description>
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    <lastBuildDate>Tue, 01 Sep 2026 00:00:00 +0700</lastBuildDate>
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      <title>Calibrate Classification Probabilities Before Using Decision Thresholds</title>
      <link>https://nalar.dev/calibrate-classification-probabilities-decision-thresholds/</link>
      <pubDate>Tue, 01 Sep 2026 00:00:00 +0700</pubDate>
      <guid>https://nalar.dev/calibrate-classification-probabilities-decision-thresholds/</guid>
      <description>&lt;p&gt;A classifier can rank examples well while producing poor probability estimates. If predictions drive cost-sensitive decisions, triage, or risk thresholds, the difference matters. A score of &lt;code&gt;0.8&lt;/code&gt; is useful as a probability only when similarly scored examples are positive about 80% of the time under the deployment distribution.&lt;/p&gt;&#xA;&lt;h2 id=&#34;separate-discrimination-from-calibration&#34;&gt;Separate discrimination from calibration&lt;/h2&gt;&#xA;&lt;p&gt;Metrics such as ROC AUC primarily measure ranking. Calibration asks whether predicted probabilities agree with observed frequencies. A model can have strong AUC and still be overconfident or underconfident.&lt;/p&gt;</description>
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