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CapyMOA: Efficient Machine Learning for Data Streams in Python

Authors :
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
Publication Year :
2025

Abstract

CapyMOA is an open-source library designed for efficient machine learning on streaming data. It provides a structured framework for real-time learning and evaluation, featuring a flexible data representation. CapyMOA includes an extensible architecture that allows integration with external frameworks such as MOA and PyTorch, facilitating hybrid learning approaches that combine traditional online algorithms with deep learning techniques. By emphasizing adaptability, scalability, and usability, CapyMOA allows researchers and practitioners to tackle dynamic learning challenges across various domains.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2502.07432
Document Type :
Working Paper