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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Jacob Montiel
- sl:arxiv_num : 2012.04740
- sl:arxiv_published : 2020-12-08T21:04:44Z
- sl:arxiv_summary : 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 the two most
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.@en
- sl:arxiv_title : River: machine learning for streaming data in Python@en
- sl:arxiv_updated : 2020-12-08T21:04:44Z
- sl:bookmarkOf : https://arxiv.org/abs/2012.04740
- sl:creationDate : 2021-01-05
- sl:creationTime : 2021-01-05T16:15:12Z
- sl:relatedDoc : http://www.semanlink.net/doc/2020/01/creme_ml_creme_online_machine_
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