<?xml version="1.0" encoding="UTF-8"?>
<term>
  <id>11502</id>
  <title>receiver-operator characteristic curve</title>
  <longtitle>IUPAC Gold Book - receiver-operator characteristic curve</longtitle>
  <doi>10.1351/goldbook.11502</doi>
  <code>11502</code>
  <status>current</status>
  <initialism><em>initialism</em>: ROC</initialism>
  <synonym><em>synonym</em>: ROC curve</synonym>
  <definitions>
    <item>
      <id>1</id>
      <text>A plot that shows the result of a test of the performance of a binary classifier by plotting the fraction of true positives identified versus the fraction of
 false positives as the discrimination threshold is varied.</text>
      <notes>
        <item>ROC curves are frequently used to compare different docking/scoring combinations or virtual screening protocols by the retrieval results from seeding a database of inactive molecules with known actives.</item>
        <item>The area under the ROC curve (AUC) provides a measure of the superiority of the model over random predictions: If the value for the AUC for a ROC curve has the value of \(\pu{0.9\!-\! 1.0}\), the fit is considered excellent, whereas an AUC of \(\pu{0.5}\) suggests that the algorithm has no discriminatory power.</item>
        <item>This is an example of using area under the curve as a measure of model performance.</item>
      </notes>
      <links>
        <item>
          <term>Boltzmann enhanced discrimination of receiver operating characteristic (BEDROC)</term>
          <url>https://goldbook.iupac.org//terms/view/11416</url>
        </item>
        <item>
          <term>area under the curve</term>
          <url>https://goldbook.iupac.org//terms/view/11407</url>
        </item>
        <item>
          <term>false positives</term>
          <url>https://goldbook.iupac.org//terms/view/11445</url>
        </item>
        <item>
          <term>true positives</term>
          <url>https://goldbook.iupac.org//terms/view/11528</url>
        </item>
      </links>
      <sources>
        <item>PAC, 2016, 88, 239. 'Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)' on page 254 (https://doi.org/10.1515/pac-2012-1204)</item>
      </sources>
    </item>
  </definitions>
  <altoutputs>
    <html>https://goldbook.iupac.org/terms/view/11502/html</html>
    <json>https://goldbook.iupac.org/terms/view/11502/json</json>
    <plain>https://goldbook.iupac.org/terms/view/11502/plain</plain>
  </altoutputs>
  <citation>Citation: 'receiver-operator characteristic curve' in IUPAC Compendium of Chemical Terminology, 5th ed. International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.11502</citation>
  <license>The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/) for individual terms.</license>
  <collection>If you are interested in licensing the Gold Book for commercial use, please contact the IUPAC Executive Director at executivedirector@iupac.org .</collection>
  <disclaimer>The International Union of Pure and Applied Chemistry (IUPAC) is continuously reviewing and, where needed, updating terms in the Compendium of Chemical Terminology (the IUPAC Gold Book). Users of these terms are encouraged to include the version of a term with its use and to check regularly for updates to term definitions that you are using.</disclaimer>
  <accessed>2026-05-31T00:20:42+00:00</accessed>
</term>
