Scikit-multiflow: A Multi-output Streaming Framework

Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem

Research output: Contribution to journalArticlepeer-review

Abstract

scikit-multiflow is a framework for learning from data streams and multi-output learning in Python. Conceived to serve as a platform to encourage the democratization of stream learning research, it provides multiple state-of-the-art learning methods, data generators and evaluators for different stream learning problems, including single-output, multi-output and multi-label. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles. Quality is enforced by complying with PEP8 guidelines, using continuous integration and functional testing. The source code is available at https://github.com/scikit-multiflow/scikit-multiflow.

Original languageEnglish
JournalJournal of Machine Learning Research
Volume19
Publication statusPublished - 1 Oct 2018
Externally publishedYes

Keywords

  • Drift Detection
  • Machine Learning
  • Multi-output
  • Python
  • Stream Data

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