Badgers: generating data quality deficits with Python

Julien Siebert, Daniel Seifert, Patricia Kelbert, Michael Kläs, Adam Trendowicz
7/10/2023
cs.LG

Official code: Fraunhofer-IESE/badgers (15 stars, BSD-3-Clause, last commit 2026-01-29).

Abstract

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

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Code Implementations(1)

Fraunhofer-IESE/badgersOfficial
BSD-3-Clause

Badgers: Bad Data Generators

154PythonMay 31, 20238 months agoBSD-3-Clause
data-analysisdata-qualitydata-science

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Cite this paper

@article{siebert2023badgers,
  title  = {Badgers: generating data quality deficits with Python},
  author = {Julien Siebert and Daniel Seifert and Patricia Kelbert and Michael Kläs and Adam Trendowicz},
  year   = {2023},
  eprint = {2307.04468},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.04468v1}
}

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