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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