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River: Machine learning for streaming data in python

journal contribution
posted on 2022-07-12, 03:03 authored by J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, A Zouitine, Heitor GomesHeitor Gomes, J Read, T Abdessalem, A Bifet
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.

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Preferred citation

Montiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H. M., Read, J., Abdessalem, T. & Bifet, A. (2021). River: Machine learning for streaming data in python. Journal of Machine Learning Research, 22.

Journal title

Journal of Machine Learning Research

Volume

22

Publication date

2021-01-01

Publication status

Published

ISSN

1532-4435

eISSN

1533-7928

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