Simplifying Hyperparameter Tuning in Online Machine Learning -- The
  spotRiverGUI
Thomas Bartz-Beielstein
Batch Machine Learning (BML) reaches its limits when dealing with very large
amounts of streaming data. This is especially true for available memory,
handling drift in data streams, and processing new, unknown data. Online
Machine Learning (OML) is an alternative to BML that overcomes the limitations
of BML. OML is able to process data in a sequential manner, which is especially
useful for data streams. The `river` package is a Python OML-library, which
provides a variety of online learning algorithms for classification,
regression, clustering, anomaly detection, and more. The `spotRiver` package
provides a framework for hyperparameter tuning of OML models. The
`spotRiverGUI` is a graphical user interface for the `spotRiver` package. The
`spotRiverGUI` releases the user from the burden of manually searching for the
optimal hyperparameter setting. After the data is provided, users can compare
different OML algorithms from the powerful `river` package in a convenient way
and tune the selected algorithms very efficiently.