Initial Selection and Subsequent Updating of OML Models

In Sect. 4.1, we describe a current best practice methodology for the initial model selection of Online Machine Learning (OML) models, taking into account that the model is continuously updated. In Sect. 4.2, we discuss possibilities for removing or changing observations/instances that have already been added to the model. We describe how completely new features can be added to the model afterwards. In addition, we show how it is ensured that the model quality is still adequate after a model update. Catastrophic forgetting (catastrophic interference) is considered in Sect. 4.3 in the OML context: The continuous updating of the OML models carries the risk that this learning is not successful if correctly learned older relationships are falsely forgotten (“de-learned”).