Дрейф данных — это изменение со временем статистических свойств или распределения входных данных, используемых моделью. Он может сделать training data менее representative of real-world conditions и снизить performance модели, если его не monitoring, investigated и corrected.
Data drift occurs when the training data no longer is representative of reality. User behavior, data collection, and the data environment can change at any time, and this can lead to a reduction in model performance.
When the statistical properties of distribution of data changes over time, impacting the performance of the model.
A side-by-side comparison of Data Drift and Model Drift. Understand how changes in input-data distribution differ from changes or degradation in model performance over time.
A side-by-side comparison of Data Drift and Concept Drift. Understand whether a model problem comes from changing input distributions or changing relationships between inputs and outcomes.