Hi Andreas,
Thanks for the answer. Hm, that's a pity. When I add it as a parameter to
fit, I get
AssertionError: Invalid parameter class_weight for estimator GridSearchCV
Does this mean class weighting isn't possible at all with GridSearch?
Thanks,
Mathias
On Fri, Feb 3, 2012 at 1:30 PM, Andreas <[email protected]> wrote:
> **
> Hi Mathias.
> As far as I know the use of class weights in grid search is not possible
> in SVC at the moment. It can be used as a parameter to fit, but this
> prevents one from using it for grid searches.
> This is a known issue and the class_weight should be moved to
> the initialization of SVC.
> I am (somewhat) working on this.
>
> Cheers,
> Andy
>
>
>
> On 02/03/2012 01:23 PM, Mathias Verbeke wrote:
>
> Hi Olivier,
>
> That's something I tried already, but then I get:
>
> AssertionError: Invalid parameter class_weight for estimator SVC
>
> Any idea what can be wrong?
>
> Thanks,
>
> Mathias
>
>
> On Fri, Feb 3, 2012 at 12:19 PM, Olivier Grisel
> <[email protected]>wrote:
>
>> 2012/2/3 Mathias Verbeke <[email protected]>:
>> > Hi Adreas,
>> >
>> > Thanks a lot; that answers my questions. Just a quick check to be sure I
>> > understand it correctly: the results in the classification report for
>> the
>> > best classifier are the ones on the test set, right?
>>
>> It print the performance measured on the test set (also known as
>> evaluation set) of the best classifier as found on the training set
>> (also known as development set).
>>
>> If you do the parameter selection and evaluation on the same dataset
>> you will be likely to overfit the hyperparameters settings and hence
>> your performance estimation will be an over-estimate of the true
>> generalization performance.
>>
>> > And another small question: could you tell me how/where I need to set
>> the
>> > class_weight parameter, since this doesn't seem to work in the regular
>> way
>> > in the fit method? Would it furthermore be possible to - besides 'auto'
>> -
>> > tune this as well with GridSearch?
>>
>> You can extend the grid search as follows (that will double the running
>> time):
>>
>> tuned_parameters = [
>> {'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
>> 'C': [1, 10, 100, 1000], 'class_weight': [None, 'auto']},
>> {'kernel': ['linear'], 'C': [1, 10, 100, 1000],
>> 'class_weight': [None, 'auto']}
>> ]
>>
>> --
>> Olivier
>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>
>>
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