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- multiow. 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 um-brella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.
| Original language | English |
|---|---|
| Journal | Journal of Machine Learning Research |
| Volume | 22 |
| Publication status | Published - 1 Jan 2021 |
Keywords
- Concept drift
- Data stream
- Online learning
- Python
- Stream learning
- Supervised learning
- Unsupervised learning