Machine Learning for Data Streams with CapyMOA

2026
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Authors: Yibin Sun; Heitor Murilo Gomes; Anton Lee; Nuwan Gunasekara; Guilherme Weigert Cassales; Jia Justin Liu; Marco Heyden; Vitor Cerqueira; Maroua Bahri; Yun Sing Koh; Bernhard Pfahringer; Albert Bifet
Published in: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
Publisher: Springer Nature Switzerland

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Abstract

CapyMOA is a cutting-edge open-source framework designed for machine learning on data streams, evolving from MOA to offer a more comprehensive ecosystem for real-time analytics. This chapter explores its key features, including integration with established tools like PyTorch and Scikit-learn, high-level evaluation functions, and dedicated visualization capabilities. It delves into the simulation of concept drift, the use of pipelines for continuous updates, and the support for various learning tasks such as supervised, semi-supervised, and unsupervised learning. Additionally, it covers anomaly detection, AutoML capabilities, and online continual learning. The text provides practical demonstrations through code snippets and screenshots, making it a valuable resource for professionals looking to leverage CapyMOA for advanced stream learning tasks. The conclusion emphasizes CapyMOA's role as a valuable resource across education, research, and development, lowering the barrier to effective streaming data analysis.

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Title: Machine Learning for Data Streams with CapyMOA
Authors: Yibin Sun
Heitor Murilo Gomes
Anton Lee
Nuwan Gunasekara
Guilherme Weigert Cassales
Jia Justin Liu
Marco Heyden
Vitor Cerqueira
Maroua Bahri
Yun Sing Koh
Bernhard Pfahringer
Albert Bifet
Publisher: Springer Nature Switzerland
Book: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
Print ISBN: 978-3-032-06128-7

Electronic ISBN: 978-3-032-06129-4

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-032-06129-4_27

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