论文标题
河流：python中流数据的机器学习
River: machine learning for streaming data in Python
论文作者
论文摘要
River是一个机器学习库，用于动态数据流和持续学习。它为不同的流学习问题提供了多种最先进的学习方法，数据生成器/变压器，性能指标和评估者。这是Python中流媒体学习的两个最受欢迎的套件合并的结果：Creme和Scikit-Multiflow。 River根据从开创性包装中学到的经验教训引入了经过改进的建筑。 River的野心是成为在流数据上进行机器学习的首选库。此外，此开源软件包带来了一个大型从业者和研究人员社区。源代码可在https://github.com/online-ml/river上找到。
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.
