Hi Mathias.
First, please note that you are looking at an "old" version of the docs.
We are in the process to include a warning.
Please refer to
http://scikit-learn.org/stable/auto_examples/grid_search_digits.html
<http://scikit-learn.org/0.9/auto_examples/grid_search_digits.html> instead.
For your first question:
I didn't write the example but this is how I understood it:
Usually when evaluating a machine learning method, you are given a
"test" and a "training"
set and train on the training set and test on the test set.
If you want to adjust hyper parameters of the method, a common way is to
do cross-validation
on the training set.
Then you still need to evaluate on an independent test set, to see how
well your parameters
generalize to something unseen.
As the digits data set does not come in a training and test part, the
StratifiedKFold split
is used to to simulate this.
Does this answer your question?
For your second question:
There is a parameter "refit" of the GridSearchCV (see the references
<http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV>
) that decides exactly that.
It is "True" by default.
Cheers,
Andy
On 02/03/2012 10:54 AM, Mathias Verbeke wrote:
Hi all,
I'm currently looking at the GridSearch example
(http://scikit-learn.org/0.9/auto_examples/grid_search_digits.html),
and I don't completely get the point of using cross-validation twice.
Why aren't the parameters and the classifier selected in on
cross-validations step?
Furthermore, I was wondering if I do a refit at the end of the
GridSearch procedure, it will train the model on the complete dataset,
so that it can be applied on the test set afterwards?
Best and thanks,
Mathias
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