Hi,
I've created a GridSearchCV like class, but instead of blindly calculating
the score at all parameter combinations, OptunitySearchCV uses cleaver
optimisation algorithms to find the best parameters. As you could guess it
leverages a library called Optunity:
http://optunity.readthedocs.org/en/latest/. The optimisation algorithms
used are specifically designed for time-expensive non-smooth functions.
I've crated a pull request which demonstrates the proof-of-concept of the
idea: https://github.com/scikit-learn/scikit-learn/pull/6662.
I think this should be included in the Scikit learn package as it can
reduce the number of times you need to run a classifier to find the 'best'
parameters. This saves a hell of a lot of time and should be part of the
standard toolkit for any machine learnist - in my opinion.
I've started this thread to discuss the topic and see if its worth
proceeding with developing a OptunitySearchCV.
Many thanks,
Josh
------------------------------------------------------------------------------
Find and fix application performance issues faster with Applications Manager
Applications Manager provides deep performance insights into multiple tiers of
your business applications. It resolves application problems quickly and
reduces your MTTR. Get your free trial!
https://ad.doubleclick.net/ddm/clk/302982198;130105516;z
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
