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 |
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Format: | Artikel |
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. |
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ISSN: | 1532-4435 1533-7928 |