# Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI

arXiv: https://arxiv.org/abs/2402.11594 · HTML: https://mlsanity.com/paper/2402.11594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [AMF: Aggregated Mondrian Forests for Online Learning](https://mlsanity.com/paper/1906.10529/) (2019)

## Abstract (arXiv)

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.