Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI

TL;DR

The `spotRiver` package provides a framework for hyperparameter tuning of OML models and the `spotRiverGUI` is a graphical user interface for the `spotRiver` package, which releases the user from the burden of manually searching for the optimal hyperparameter setting.

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.

Figures (17)

• Figure 1: spotriver GUI
• Figure 2: spotRiverGUI when forest.AMFClassifier is selected
• Figure 3: spotRiverGUI when tree.HoeffdingAdaptiveTreeClassifier is selected
• Figure 4: Output from the spotRiverGUI when Bananas data is selected for the Show Data option
• Figure 5: Visualization of the train data. Output from the spotRiverGUI when Bananas data is selected for the Show Data option