Drift Detection and Handling

Structural changes (“drift”) in the data cause problems for many algorithms. Based on the drift definitions given in Chap. 1 , methods for drift detection and handling are discussed. For the algorithms presented in Chap. 2 , it is clarified to what extent concept drift is reacted to. In turn, the extent to which catastrophic forgetting is an issue is described in Sect. 4.3 . Section 3.1 describes three architectures for implementing drift detection algorithms. Basic properties of window-based approaches are presented in Sect. 3.2. Section 3.4 presents commonly used drift detection techniques. Section 3.4 describes how the drift detection techniques introduced in Sect. 3.3 are used in Online Machine Learning (OML) algorithms and summarizes the tree-based OML techniques implemented in the River package. Section 3.5 introduces scaling methods for handling drift.