Hi Lars,
Thanks for your answer.
I removed tol and I start using per-kernel grids.
It is not clear, though, what's a good range for 'degree' , 'gamma', and
'coef0'. In addition, I believe that the "zoom in" -approach may not lead
to the global maximum but rather to some local ones. Is that correct? What
should I do with the aforementioned parameters? Should I use step 10, step
1, step 0.1, step 0.01 or what?
On Mon, Oct 13, 2014 at 1:05 PM, Lars Buitinck <larsm...@gmail.com> wrote:
> 2014-10-13 10:37 GMT+02:00 Adamantios Corais <adamantios.cor...@gmail.com
> >:
> > I am running into the problem that the hyperparameters of my svm.SVC()
> are
> > too wide such that the GridSearchCV() never gets completed! One idea is
> to
> > use RandomizedSearchCV() instead. But again, my dataset is relative big
> such
> > that 500 iterations take about 1 hour! My question is, what is a good
> set-up
> > (in terms of the range of values for each hyperparameter) in GridSearchCV
> > (or RandomizedSearchCV) in order to stop wasting resources... In other
> > words, how to decide whether or not e.g. C values above 100 make sense
> > and/or step of 1 is neither big not small? Any help is very much
> > appreciated. This is the set-up am currently using:
>
> Start off by not grid-searching tol. It determines when to stop
> learning, not what the model should look like. You're probably fitting
> practically the same set of models ten times by using many similar
> values for tol.
>
> Second, use per-kernel grids to prevent searching along irrelevant
> dimensions:
>
> parameters = [
> {'kernel': ['linear'], 'C': C_values, ...}
> {'kernel': ['rbf'], 'C': C_values, 'gamma': gamma_values, ...}
> ]
>
> The linear kernel ignores degree, gamma and coef0. The RBF kernel
> ignores degree and coef0. GridSearchCV doesn't know this, so you're
> again fitting the same models many times.
>
> As for the actual question: C is usually determined by first using a
> coarse, exponential range like 'C': [1, 10, 100, 1000]. You can then
> "zoom in" on the optimum in a second grid-search (if it was 10, try
> [5, 20, 50]).
>
>
> ------------------------------------------------------------------------------
> Meet PCI DSS 3.0 Compliance Requirements with EventLog Analyzer
> Achieve PCI DSS 3.0 Compliant Status with Out-of-the-box PCI DSS Reports
> Are you Audit-Ready for PCI DSS 3.0 Compliance? Download White paper
> Comply to PCI DSS 3.0 Requirement 10 and 11.5 with EventLog Analyzer
> http://p.sf.net/sfu/Zoho
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
------------------------------------------------------------------------------
Meet PCI DSS 3.0 Compliance Requirements with EventLog Analyzer
Achieve PCI DSS 3.0 Compliant Status with Out-of-the-box PCI DSS Reports
Are you Audit-Ready for PCI DSS 3.0 Compliance? Download White paper
Comply to PCI DSS 3.0 Requirement 10 and 11.5 with EventLog Analyzer
http://p.sf.net/sfu/Zoho
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
