scikit-multiflow is inspired by MOA, the most popular open source framework for machine learning for data streams, and MEKA, an open source implementation of methods for multi-label learning.
scikit-multiflow is also inspired on scikit-learn, the most popular framework for machine learning in Python. Following the SciKits philosophy,
scikit-multiflow is an open source machine learning framework for multi-output/multi-label and stream data.
scikit-multiflow is implemented in Python given its increasing popularity in the Machine Learning community. It complements
scikit-learn, whose primary focus is batch learning, expanding the set of Machine Learning tools on this platform. In its current state,
scikit-multiflow contains stream generators, stream classifiers for multi-output/multi-target, change detectors and evaluation methods.
A mailing list is available for users to learn, teach and ask questions: scikit-multiflow users
As an open source project, we welcome contributions from the community. Please refer to the GitHub Repository for further information.
If you want to cite
scikit-multiflow in a publication, please refer to the following paper: