Hi Michael,
Thanks for the clarifications.
Is there a way to make prediction, once grid search is done? Right now I’m
getting the error
'GridSearchCV' object has no attribute 'best_estimator_'
And I’ve seen other people reporting the same error. If not possible, is there
a minimal example of k-fold cross-validation with prediction?
Thank you,
From: Michael Eickenberg [mailto:[email protected]]
Sent: Wednesday, July 23, 2014 12:08 PM
To: [email protected]
Subject: Re: [Scikit-learn-general] GridSearchVC with SVM
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]<mailto:[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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