deriva: Tidy Drift Detection for Monitored Machine Learning Models

Detects concept drift and data drift in streams produced by deployed machine learning models, using a tidy interface that composes with the 'tidymodels' ecosystem. Detectors are specified, fitted on a baseline period, and advanced over new batches of observations, returning tibbles annotated with warning and drift flags. A catalogue of 22 sequential drift detectors is provided. Error-based methods include the Drift Detection Method (DDM) of Gama et al. (2004) doi:10.1007/978-3-540-28645-5_29 , the Early Drift Detection Method (EDDM) of Baena-Garcia et al. (2006), the Hoeffding's inequality based Drift Detection Methods (HDDM) of Frias-Blanco et al. (2015) doi:10.1109/TKDE.2014.2345382 , and the Exponentially Weighted Moving Average (EWMA) chart of Ross et al. (2012) doi:10.1016/j.patrec.2011.08.019 . Distribution-based methods include Adaptive Windowing (ADWIN) of Bifet and Gavalda (2007) doi:10.1137/1.9781611972771.42 , Kolmogorov-Smirnov Windowing (KSWIN) of Raab et al. (2020) doi:10.1016/j.neucom.2019.11.111 , and the Page-Hinkley test of Page (1954) doi:10.1093/biomet/41.1-2.100 .

See also

Useful links:

• https://github.com/bonijoao/deriva

• https://bonijoao.github.io/deriva/

• Report bugs at https://github.com/bonijoao/deriva/issues

Author

Maintainer: João Paulo Assis Bonifácio jpab.27@hotmail.com (ORCID)

Authors:

• Geraldo Magela da Cruz Pereira geraldo.pereira@ufla.br (ORCID)

• Pedro Mambelli Fernandes pedromambelli@gmail.com (ORCID)