https://github.com/scikit-learn/ml-benchmarks

Benchmarks for various machine learning packages
https://github.com/scikit-learn/ml-benchmarks

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Benchmarks for various machine learning packages

• Host: GitHub
• URL: https://github.com/scikit-learn/ml-benchmarks
• Owner: scikit-learn
• Created: 2011-02-23T13:12:44.000Z (almost 15 years ago)
• Default Branch: master
• Last Pushed: 2014-07-04T09:34:52.000Z (over 11 years ago)
• Last Synced: 2024-10-29T14:21:48.777Z (about 1 year ago)
• Language: Python
• Homepage: http://scikit-learn.github.com/ml-benchmarks/
• Size: 4.68 MB
• Stars: 91
• Watchers: 46
• Forks: 49
• Open Issues: 3
• Metadata Files:
  • Readme: README.rst

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README

          
Benchmarks for various machine learning packages

==================================================

Collection of benchmarks comparing various python-based machine

learning packages.

This is meant to work with the development version of the libraries

scikits.learn, mlpy, pybrain, pymvpa, mdp and shogun. It might be hard

to get all packages working on the same machine, but benchmarks are

designed so that if something fail it will just print the exception

and go to the next one.

To execute a benchmark, just type from the prompt::

    $ python benchmarks/bench_$name.py

and you will se as output the mean and std deviation for the timing of

running the benchmark 10 times with its extreme values removed.

Results

----------

The latest maintained results of these benchmarks can be found on

http://scikit-learn.github.com/ml-benchmarks/

Others results of running these benchmarks on different boxes and with different software versions can 

be found on:

  - http://scikit-learn.sourceforge.net/ml-benchmarks/

  - http://fseoane.net/ml-benchmarks/

  - http://packages.python.org/milk/benchmarks.html

They differ because they are run with different versions of the packages, and different

compilation settings (e.g. linear algebra packs).

References

----------

  - scikit-learn : http://scikit-learn.sourceforge.net

  - MDP : http://mdp-toolkit.sourceforge.net/

  - PyMVPA : http://pymvpa.org

  - MLPy : https://mlpy.fbk.eu/

  - Shogun: http://www.shogun-toolbox.org/

  - PyBrain : http://pybrain.org/

  - Milk : http://luispedro.org/software/milk

  - Orange : http://orange.biolab.si/

Misc

----

Author: Fabian Pedregosa 

License: Simplified BSD