Titlebook: Online Machine Learning; A Practical Guide wi Eva Bartz,Thomas Bartz-Beielstein Book 2024 The Editor(s) (if applicable) and The Author(s),

莊嚴(yán)

MELD

,Drift Detection and?Handling,ion and handling are discussed. For the algorithms presented in Chap. ., 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. .. Section?. describes three architectures for implementing drift detection algo

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蒙太奇

完成才能戰(zhàn)勝

,Special Requirements for?Online Machine Learning Methods,spect to typical practice challenges such as missing data (Sect.?.), categorical attributes (Sect.?.), outliers (Sect.?.), imbalanced data (Sect.?.), or an extremely large number of variables (Sect.?.). Section?. describes important aspects such as fairness (Fair Machine Learning (ML)) or interpreta

PANIC

,Practical Applications of?Online Machine Learning,strated by means of domain-specific examples from different application fields. One of these surveyed application fields is official statistics (Sect.?.). Section?. shows, that OML offers forward-looking potential for official statistics, but presently also comes with a lot of challenges. Especially

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An Experimental Comparison of Batch and Online Machine Learning Algorithms,Machine Learning (OML) models for predicting the demand for bicycles at a bike-sharing station. The second study (Sect. .) investigates the use of BML and OML models for prediction when very large data sets are available and drift is present. The synthetic Friedman-drift data set (see Definition .)

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Hyperparameter Tuning, of “splitters” are available for Hoeffding trees to generate subtrees. There are different methods for limiting the tree size in order to keep the time and memory requirements within reasonable limits. In addition, there are many other parameters, so that a manual search for the optimal hyperparame

ABYSS

Summary and Outlook, discussed and concrete recommendations for OML practice are given. The importance of a suitable comparison methodology for Batch Machine Learning (BML) and OML methods is highlighted to avoid “comparing apples to oranges”. We also point out the great potential of OML that is available through the d