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>.
