Project Summary
Python module integrating various machine learning algorithms under a common interface. It offers a wide range of methods such as Support Vector Machines, linear models (L1, L2 penalized), logistic regression, gaussian mixture models and more. The large number of algorithms aleady implemented allows for easy comparison of accuracy and performance of various algorithms.
Quick Reference
- Project Links:
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Homepage
Documentation
- Code Locations:
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git://github.com/scikit-learn/sc...
- Licenses:
- BSD-4-Clause-UC
- Similar Projects:
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- Managers:
- Fabian Pedregosa
In a Nutshell, scikit learn...
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has a codebase with a long source history
maintained by a very large development team
with decreasing Y-O-Y commits -
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took an estimated 83 years of effort (COCOMO model)
starting with its first commit in January, 2010
ending with its most recent commit 14 days ago
Activity
30 Day Summary May 8 2013 — Jun 7 2013
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12 Month Summary Jun 7 2012 — Jun 7 2013
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Community
Copyright
©
2013
Black Duck Software, Inc.
and its contributors, Some Rights Reserved. Unless otherwise marked, this work is licensed under a
Creative Commons Attribution 3.0 Unported License
. Ohloh
®
and the Ohloh logo are trademarks of
Black Duck Software, Inc.
in the United States and/or other jurisdictions. All other trademarks are the property of their respective holders.