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Raphael Sourty

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JMLR Journal 2021 Journal Article

River: machine learning for streaming data in Python

  • Jacob Montiel
  • Max Halford
  • Saulo Martiello Mastelini
  • Geoffrey Bolmier
  • Raphael Sourty
  • Robin Vaysse
  • Adil Zouitine
  • Heitor Murilo Gomes

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-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. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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