River: Machine Learning for Streaming Data in Python
Abstract
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of two popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.
Cite
Text
Montiel et al. "River: Machine Learning for Streaming Data in Python." Machine Learning Open Source Software, 2021.Markdown
[Montiel et al. "River: Machine Learning for Streaming Data in Python." Machine Learning Open Source Software, 2021.](https://mlanthology.org/mloss/2021/montiel2021jmlr-river/)BibTeX
@article{montiel2021jmlr-river,
title = {{River: Machine Learning for Streaming Data in Python}},
author = {Montiel, Jacob and Halford, Max and Mastelini, Saulo Martiello and Bolmier, Geoffrey and Sourty, Raphael and Vaysse, Robin and Zouitine, Adil and Gomes, Heitor Murilo and Read, Jesse and Abdessalem, Talel and Bifet, Albert},
journal = {Machine Learning Open Source Software},
year = {2021},
pages = {1-8},
volume = {22},
url = {https://mlanthology.org/mloss/2021/montiel2021jmlr-river/}
}