River: machine learning for streaming data in Python
Jacob Montiel
Max Halford
S. Mastelini
Geoffrey Bolmier
Raphael Sourty
Robin Vaysse
Adil Zouitine
Heitor Murilo Gomes
Jesse Read
T. Abdessalem
Albert Bifet

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 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.
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