CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913025507721216 |
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| author | Gomes, Heitor Murilo Lee, Anton Gunasekara, Nuwan Sun, Yibin Cassales, Guilherme Weigert Liu, Justin Heyden, Marco Cerqueira, Vitor Bahri, Maroua Koh, Yun Sing Pfahringer, Bernhard Bifet, Albert |
| author_facet | Gomes, Heitor Murilo Lee, Anton Gunasekara, Nuwan Sun, Yibin Cassales, Guilherme Weigert Liu, Justin Heyden, Marco Cerqueira, Vitor Bahri, Maroua Koh, Yun Sing Pfahringer, Bernhard Bifet, Albert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python Gomes, Heitor Murilo Lee, Anton Gunasekara, Nuwan Sun, Yibin Cassales, Guilherme Weigert Liu, Justin Heyden, Marco Cerqueira, Vitor Bahri, Maroua Koh, Yun Sing Pfahringer, Bernhard Bifet, Albert Machine Learning 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. |
| title | CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.07432 |