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
  • , Jesse Read
  • , Talel Abdessalem
  • , Albert Bifet

Research output: Contribution to journalArticlepeer-review

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 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.

Original languageEnglish
JournalJournal of Machine Learning Research
Volume22
Publication statusPublished - 1 Jan 2021

Keywords

  • Concept drift
  • Data stream
  • Online learning
  • Python
  • Stream learning
  • Supervised learning
  • Unsupervised learning

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