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        "author": "Bhargav Srinivasa Desikan",
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            "Development Status :: 4 - Beta",
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            "Programming Language :: Python",
            "Topic :: Scientific/Engineering"
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        "description": "|Travis Status| |Coverage Status| |Python27| |Python35|\n\npycobra\n-------\n\npycobra is a python library for ensemble learning. It serves as a\ntoolkit for regression and classification using these ensembled\nmachines, and also for visualisation of the performance of the new\nmachine and constituent machines. Here, when we say machine, we mean any\npredictor or machine learning object - it could be a LASSO regressor, or\neven a Neural Network. It is scikit-learn compatible and fits into the\nexisting scikit-learn ecosystem.\n\npycobra offers a python implementation of the COBRA algorithm introduced\nby Biau et al. (2016) for regression.\n\nAnother algorithm implemented is the EWA (Exponentially Weighted\nAggregate) aggregation technique (among several other references, you\ncan check the paper by Dalalyan and Tsybakov (2007).\n\nApart from these two regression aggregation algorithms, pycobra\nimplements a version of COBRA for classification. This procedure has\nbeen introduced by Mojirsheibani (1999).\n\npycobra also offers various visualisation and diagnostic methods built\non top of matplotlib which lets the user analyse and compare different\nregression machines with COBRA. The Visualisation class also lets you\nuse some of the tools (such as Voronoi Tesselations) on other\nvisualisation problems, such as clustering.\n\nDocumentation and Examples\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe\n`notebooks <https://github.com/bhargavvader/pycobra/tree/master/notebooks>`__\ndirectory showcases the usage of pycobra, with examples and basic usage.\nThe `documentation <https://bhargavvader.github.io>`__ page further\ncovers how to use pycobra.\n\nInstallation\n~~~~~~~~~~~~\n\nRun ``pip install pycobra`` to download and install from PyPI.\n\nRun ``python setup.py install`` for default installation.\n\nRun ``python setup.py test`` to run all tests.\n\nRun ``pip install .`` to install from source.\n\nDependencies\n~~~~~~~~~~~~\n\n-  Python 2.7+, 3.4+\n-  numpy, scipy, scikit-learn, matplotlib\n\nReferences\n~~~~~~~~~~\n\n-  G. Biau, A. Fischer, B. Guedj and J. D. Malley (2016), COBRA: A\n   combined regression strategy, Journal of Multivariate Analysis.\n-  M. Mojirsheibani (1999), Combining Classifiers via Discretization,\n   Journal of the American Statistical Association.\n-  A. S. Dalalyan and A. B. Tsybakov (2007) Aggregation by exponential\n   weighting and sharp oracle inequalities, Conference on Learning\n   Theory.\n\n.. |Travis Status| image:: https://travis-ci.org/bhargavvader/pycobra.svg?branch=master\n   :target: https://travis-ci.org/bhargavvader/pycobra\n.. |Coverage Status| image:: https://coveralls.io/repos/github/bhargavvader/pycobra/badge.svg?branch=master\n   :target: https://coveralls.io/github/bhargavvader/pycobra?branch=master\n.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg\n   :target: https://pypi.python.org/pypi/pycobra\n.. |Python35| image:: https://img.shields.io/badge/python-3.5-blue.svg\n   :target: https://pypi.python.org/pypi/pycobra\n",
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