River: machine learning for streaming data in Python

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 packag...

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Veröffentlicht in:Journal of machine learning research 2021, Vol.22, p.1-8
Hauptverfasser: Montiel, Jacob, Halford, Max, Mastelini, Saulo Martiello, Bolmier, Geoffrey, Sourty, Raphaël, Vaysse, Robin, Zouitine, Adil, Gomes, Heitor Murilo, Read, Jesse, Abdessalem, Talel, Bifet, Albert
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Sprache:eng
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Zusammenfassung: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 scikitmultiflow. 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.
ISSN:1532-4435
1533-7928