Hi Roberto,
I'm no expert by any means, but I was reading a blog post the other day that
talked about using Random Search vs Grid Search. The gist of the article is
that, since you can feed distributions to Random Search and it selects values
randomly over the number of iterations you choose, it is a better initial
choice when you're not sure which parameters/combinations to use (which is
usually my case :)) and you'll end up with Random Search finding more useful
parameters faster than if you tell Grid Search to search over combinations
(some of which may have no potential to help you). Then when you see the
results of the Random Search, you can use that information to search a narrower
range of values/parameters (a finer grid) exhaustively using Grid Search.
Unfortunately, I thought I bookmarked the article but I can't find it. I'll
keep looking though and send it out if I do.
Additionally, the docs for the individual estimators in Sklearn tell you what
parameters are not valid with each other, so you wouldn't want to put those
parameters together in your param_grid dictionary. For your dictionary (as
others have already mentioned) just make sure that you only provide options in
each of your dictionaries that can be used together. You can pass a list of
dictionaries to param_grid like Sebastian just demonstrated.
Check the links below as well, Random Search comes up with just about the same
results as Grid Search, but faster/more efficiently. Hope this helps.
Scikit Docs:
http://scikit-learn.org/stable/modules/grid_search.html#grid-search-tips
http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html#example-model-selection-randomized-search-py
-Jason
From: Pagliari, Roberto [mailto:rpagli...@appcomsci.com]
Sent: Tuesday, April 07, 2015 9:24 AM
To: scikit-learn-general@lists.sourceforge.net
Subject: [Scikit-learn-general] CV with SVM
not all combinations of cost/loss functions and dual are possible with SVM.
when performing grid search with CV, does sklearn skip invalid combinations?
Thank you,
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