Project details for pycobra regression analysis and ensemble toolkit

Logo pycobra regression analysis and ensemble toolkit 0.2.2

by bhargavvader - December 29, 2017, 13:57:46 CET [ Project Homepage BibTeX Download ]

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pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem.

pycobra offers a python implementation of the COBRA algorithm introduced by Biau et al. (2016) for regression.

Another algorithm implemented is the EWA (Exponentially Weighted Aggregate) aggregation technique (among several other references, you can check the paper by Dalalyan and Tsybakov (2007).

Apart from these two regression aggregation algorithms, pycobra implements a version of COBRA for classification. This procedure has been introduced by Mojirsheibani (1999).

pycobra also offers various visualisation and diagnostic methods built on top of matplotlib which lets the user analyse and compare different regression machines with COBRA. The Visualisation class also lets you use some of the tools (such as Voronoi Tesselations) on other visualisation problems, such as clustering.


  • G. Biau, A. Fischer, B. Guedj and J. D. Malley (2016), COBRA: A combined regression strategy, Journal of Multivariate Analysis.
  • M. Mojirsheibani (1999), Combining Classifiers via Discretization, Journal of the American Statistical Association.
  • A. S. Dalalyan and A. B. Tsybakov (2007) Aggregation by exponential weighting and sharp oracle inequalities, Conference on Learning Theory.
Changes to previous version:

pycobra is further pep8 compliant, has improved tests and more plotting options.

BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Agnostic
Data Formats: Agnostic, Python
Tags: Classification, Regression, Visualization, Classifiaction, Machine Learning, Ensemble Methods, Ensemble Learning
Archive: download here

Other available revisons

Version Changelog Date

pycobra is further pep8 compliant, has improved tests and more plotting options.

December 29, 2017, 13:57:46

Project is now fully scikit-learn compatible, implements 2 new predictor aggregation algorithms, more Jupyter notebooks and examples, and continuous integration for tests.

July 24, 2017, 17:28:39

Initial Announcement on

April 19, 2017, 15:04:14


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