River: machine learning for streaming data in Python
This library aims to provide a unified and comprehensive platform for practitioners and researchers working with streaming data in machine learning.
River is a new Python library for machine learning on dynamic data streams and continual learning, merging Creme and scikit-multiflow. It offers state-of-the-art learning methods, data tools, and performance evaluators for various stream learning problems.
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.