On 01/23/2015 07:35 PM, Aardvark Zebra wrote:
Thanks! It seems very counter-intuitive. If I'm calling fit(), it
should return the
best model after grid search (especially if I'm giving refit=True as a
parameter).
Unfortunately, printing the return value doesn't show the best
estimator; even
the get_params() call doesn't reveal it. One has to access the
"hidden" _best_estimator
parameter.
It is not hidden (best_estimator_, not _best_estimator) and well documented.
you can also look at best_score_ (also not hidden and well documented).
I am not sure why you would expect get_params to provide it, as this
give you the parameters you set on the model in construction,
which is the same that __repr__ shows.
The __repr__ never shows any thing that is learned, just the parameters
it was constructed with.
On Thu, Jan 22, 2015 at 5:33 PM, Joel Nothman <joel.noth...@gmail.com
<mailto:joel.noth...@gmail.com>> wrote:
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
<mailto: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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