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  <http://id.crossref.org/issn/2451-8492> dct:title "Data Science" .
  
  <https://doi.org/10.3233/DS-240059> dct:abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case.";
    dct:date "2024-06-26";
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    foaf:name "Marcel R. Haas" .
  
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    foaf:name "Lisette Sibbald" .
  
  <https://ror.org/04b8v1s79> foaf:name "Department of Methodology and Statistics and Department of Cognitive Neuropsychology, Tilburg University, Prof. Cobbenhagenlaan 125, 5037 DB Tilburg, The Netherlands" .
  
  <https://ror.org/04dkp9463> foaf:name "Business Intelligence, University of Amsterdam, Spui 21, 1012WX Amsterdam, The Netherlands" .
  
  <https://ror.org/05xvt9f17> foaf:name "Public Health and Primary Care, Leiden University Medical Center, Albinusdreef 2, The Netherlands" .
  
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