Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI

• URL: http://arxiv.org/abs/2402.11594v1
• Date: Sun, 18 Feb 2024 14:12:15 GMT
• Title: Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI
• Authors: Thomas Bartz-Beielstein
• Abstract summary: Online Machine Learning (OML) is an alternative to Batch Machine Learning (BML) OML is able to process data in a sequential manner, which is especially useful for data streams.
• Score: 0.5439020425819
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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