To answer 1): yes, if you set cv=number, then it will do K-fold
cross-validation with that number of folds. You can do this explicitly by
using
from sklearn.cross_validation import KFold
cv = KFold(len(data), 6)
and pass cv as an argument to GridSearchCV.
To answer question 2 I think we need some clarification: What exactly do
you want to have averaged? The way GridSearchCV works is that for a given
parameter, it will average the scores over all folds, and choose the
parameter that does best in this metric. Is this what you want? (There are
other ways of doing averaging, so we need to be sure we are talking about
the same thing)
Hope that helps,
Michael
On Wed, Jul 23, 2014 at 5:51 PM, Pagliari, Roberto <[email protected]>
wrote:
> This is an example about how to perform gridsearch with SVM.
>
>
>
> *>>> **from* *sklearn* *import* svm, grid_search, datasets
>
> *>>> *iris = datasets.load_iris()
>
> *>>> *parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>
> *>>> *svr = svm.SVC()
>
> *>>> *clf = grid_search.GridSearchCV(svr, parameters)
>
> *>>> *clf.fit(iris.data, iris.target)
>
>
>
> I have the following two questions:
>
>
>
> 1. If I set cv=6, will k-fold automatically be selected? If not,
> how to I set it?
>
> 2. How do I specify that the best estimator parameters should be
> average of the output from the k folds, or is it done by default?
>
>
>
> Thank you,
>
>
>
>
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