scikit-learn / ml-benchmarks

Benchmarks for various machine learning packages

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 <[e-mail]>

License: Simplified BSD