Badgers: generating data quality deficits with Python
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 10 Jul 2023.
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7 of 14 references.
References · 7
- 2018Albumentations: fast and flexible image augmentationscited by 560
- 2020Learning under Concept Drift: A Reviewcited by 477
- 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
- 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 218
- 2016Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learningcited by 195
- 2021A Survey on Data Augmentation for Text Classificationcited by 111
- 2020Generating Artificial Outliers in the Absence of Genuine Ones -- a Surveycited by 4
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Citing papers per year: 2023: 1.
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