ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2012.04740
11
191

River: machine learning for streaming data in Python

8 December 2020
Jacob Montiel
Max Halford
S. Mastelini
Geoffrey Bolmier
Raphael Sourty
Robin Vaysse
Adil Zouitine
Heitor Murilo Gomes
Jesse Read
T. Abdessalem
Albert Bifet
    AI4TS
    AI4CE
ArXivPDFHTML
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.

View on arXiv
Comments on this paper