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arXiv · 2502.07432

CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python

Heitor Murilo Gomes·Anton Lee·Nuwan Gunasekara·Yibin Sun·Guilherme Weigert Cassales·Justin Liu·Marco Heyden·Vitor Cerqueira·Maroua Bahri·Yun Sing Koh·Bernhard Pfahringer·Albert Bifet
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Abstract

CapyMOA is an open-source Python library for efficient machine learning on data streams and online continual learning. It provides a structured framework for real-time learning, supporting adaptive models that evolve over time. CapyMOA's architecture allows integration with frameworks such as MOA, scikit-learn and PyTorch, enabling the combination of high-performance online algorithms with modern deep learning techniques. By emphasizing efficiency, scalability, and usability, CapyMOA allows researchers and practitioners to tackle dynamic learning challenges across various domains. Website: https://capymoa.org. GitHub: https://github.com/adaptive-machine-learning/CapyMOA.

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Heitor Murilo Gomes, Anton Lee, Nuwan Gunasekara, Yibin Sun, Guilherme Weigert Cassales, Justin Liu, Marco Heyden, Vitor Cerqueira, Maroua Bahri, Yun Sing Koh, Bernhard Pfahringer, Albert Bifet. 2025-02-11. CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python. https://arxiv.org/abs/2502.07432

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