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Data Science
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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.
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2024
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Measuring Data Drift with the Unstable Population Indicator
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Marcel R. Haas
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Lisette Sibbald
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Department of Methodology and Statistics and Department of Cognitive Neuropsychology, Tilburg University, Prof. Cobbenhagenlaan 125, 5037 DB Tilburg, The Netherlands
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Business Intelligence, University of Amsterdam, Spui 21, 1012WX Amsterdam, The Netherlands
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Public Health and Primary Care, Leiden University Medical Center, Albinusdreef 2, The Netherlands
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