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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