Special Requirements for Online Machine Learning Methods

This chapter investigates whether Online Machine Learning (OML) algorithms require special steps and considerations compared to batch learning with respect to typical practice challenges such as missing data (Sect. 6.1), categorical attributes (Sect. 6.2), outliers (Sect. 6.3), imbalanced data (Sect. 6.4), or an extremely large number of variables (Sect. 6.5). Section 6.6 describes important aspects such as fairness (Fair Machine Learning (ML)) or interpretability (Interpretable ML) in the context of OML algorithms.