River: machine learning for streaming data in Python
- URL: http://arxiv.org/abs/2012.04740v1
- Date: Tue, 8 Dec 2020 21:04:44 GMT
- Title: River: machine learning for streaming data in Python
- Authors: Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey
Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes,
Jesse Read, Talel Abdessalem, Albert Bifet
- Abstract summary: 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.
- Score: 9.683946022036684
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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