River: machine learning for streaming data in Python

By Jacob Montiel, Max Halford, S. Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem and 1 more. Journal of machine learning research, 2020

313 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/e7924a71ff89f37f66298a6b42bcd26fa7c0f33b