That's not the learnt estimator. You're looking at the initial input (i.e.
the parameters that are or are not changed during the search). The learnt
estimators are cloned from that one, and the best is stored at
clf.best_estimator_ (if refit=True).
Cheers, Joel
On 23 January 2015 at 12:20, Aardvark Zebra <exma...@gmail.com> wrote:
> I just started with s-l, and was playing around with it in iPython using
> the Iris set.
>
> I created an SVM classifier thusly:
>
> clf = grid_search.GridSearchCV(svm.SVC(), param_grid={'kernel':('linear',
> 'rbf'), 'C':arange(10,20)})
>
> (Basically, I want to grid-search for different parameters of "C", and 2
> kernel functions).
>
> However, when I train it (fit) like this:
> clf.fit(iris.data, iris.target)
>
> I get the following back:
>
> GridSearchCV(cv=None,
> estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
> degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
> random_state=None,
> shrinking=True, tol=0.001, verbose=False),
> fit_params={}, iid=True, loss_func=None, n_jobs=1,
> param_grid={'kernel': ('linear', 'rbf'), 'C': array([10, 11, 12, 13, 14,
> 15, 16, 17, 18, 19])},
> pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,
> verbose=1)
>
>
> The learned estimator has "C" = 1.0 ; when the grid-search was for the range
> 10 .. 19 (it's just an example...).
>
> Shouldn't the value of C be in this range?
>
>
> Thanks.
>
>
>
>
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GigeNET is offering a free month of service with a new server in Ashburn.
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Higher redundancy.Lower latency.Increased capacity.Completely compliant.
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