dataafdrift
Dataafdrift is a term used in data science to describe changes in the statistical properties of data over time or across data batches that can affect the performance of machine learning models. It typically refers to shifts in the inputs rather than changes in the underlying relationship between inputs and targets. In practice, dataafdrift can occur without immediate changes to the target label generation, which distinguishes it from broader concept drift.
Dataafdrift can arise from several sources, including changes in data collection methods, updates to data pipelines
Detection and measurement of dataafdrift rely on statistical comparisons between historical training data and current data.
Mitigation strategies focus on reducing sensitivity to distributional changes. Approaches include scheduled or online retraining, incremental
See also: data drift, concept drift, model monitoring, data quality.