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Pycobra: A python toolbox for ensemble learning and visualisation

Guedj, B; Desikan, BS; (2018) Pycobra: A python toolbox for ensemble learning and visualisation. Journal of Machine Learning Research , 18 (190) pp. 1-5. Green open access

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Abstract

We introduce pycobra, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a predict method is given), and visualisation tools such as Voronoi tessellations. pycobra is fully scikit-learn compatible and is released under the MIT open-source license. pycobra can be downloaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at https://github.com/bhargavvader/pycobra and official documentation website is https://modal.Lille.inria.fr/pycobra.

Type: Article
Title: Pycobra: A python toolbox for ensemble learning and visualisation
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.jmlr.org/papers/v18/17-228.html
Language: English
Additional information: © 2018 Benjamin Guedj and Bhargav Srinivasa Desikan. This is an Open Access article distributed under a Creative Commons CC-BY 4.0 licence, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v18/17-228.html
Keywords: ensemble methods, machine learning, Voronoi tesselation, Python, open source software
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10064474
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