Badgers: generating data quality deficits with Python
Julien Siebert Daniel Seifert Patricia Kelbert Michael Kläs Adam Trendowicz
Generating context specific data quality deficits is necessary to
experimentally assess data quality of data-driven (artificial intelligence (AI)
or machine learning (ML)) applications. In this paper we present badgers, an
extensible open-source Python library to generate data quality deficits
(outliers, imbalanced data, drift, etc.) for different modalities (tabular
data, time-series, text, etc.). The documentation is accessible at
https://fraunhofer-iese.github.io/badgers/ and the source code at
https://github.com/Fraunhofer-IESE/badgers