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links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

http://purl.org/dc/terms/title title

Measuring Data Drift with the Unstable Population Indicator

Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

http://www.w3.org/1999/02/22-rdf-syntax-ns#type type

http://purl.org/spar/fabio/ResourcePaper ResourcePaper

Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

http://purl.org/pav/authoredBy authoredBy

https://orcid.org/0009-0003-5030-0108 0009-0003-5030-0108

Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

http://purl.org/dc/terms/isPartOf isPartOf

http://id.crossref.org/issn/2451-8492 2451-8492

Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

http://purl.org/dc/terms/date date

2024

Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

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Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

http://purl.org/pav/authoredBy authoredBy

https://orcid.org/0000-0003-2581-8370 0000-0003-2581-8370

Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion

http://purl.org/dc/terms/abstract 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.

Tobias Kuhn

2024-02-29T09:56:50.813Z

https://w3id.org/kpxl/ios/ds/np/RAw9z6Zz148e1e6v4dyLObRaoOP97lMMPSLePvaGUMObA RAw9z6Zz14

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http://purl.org/nanopub/x/introduces introduces

Tobias Kuhn

2024-02-29T09:56:50.813Z