Principles of Drift Detection and ML Solution Retraining
摘要
This chapter introduces the concept of drift as a change in the statistical distributions of observed data or in the performance of an ML model. Several representative types of drifts are discussed, with illustrative examples. It presents the mathematical notation used in Chaps. 7 – 9 for describing datasets and probability distributions.