What do expect a classifier to predict on a label that it has never seen
during training? If there were structure in the target, such as an order,
then an appropriate regression may be able to infer unseen targets due to
this structure. But in classification this information is entirely absent.
Michael
On Fri, May 1, 2015 at 5:07 PM, Pagliari, Roberto <rpagli...@appcomsci.com>
wrote:
> Hi Sebastian,
> if classes/labels are the same for both training and test, that should not
> be a problem. I've done that and never seen any issues. As far as I can
> see, scikit learn automatically maps classes into numbers from 0 to number
> of classes -1, which is something Spark, for example, does not do.
>
> With different set of classes, the simplest thing is to remove the ones in
> the test that do not appear in the training, to avoid messing with the
> confusion matrix [ in my case, different label numbers are really different
> classes ]
>
>
> ________________________________________
> From: Sebastian Raschka [se.rasc...@gmail.com]
> Sent: Thursday, April 30, 2015 11:08 PM
> To: scikit-learn-general@lists.sourceforge.net
> Subject: Re: [Scikit-learn-general] class label hashing
>
> Roberto, I am not sure if this causes problems regarding the
> implementation, but in any case, I'd recommend you to use the LabelEncoder
> to have your classes mapped to a fixed range, e.g., 0, 1, 2, 3, 4, 5. And
> having different class labels in training and test set that reference to
> the same class is not good practice and could cause all kinds of problems.
> I just wouldn't risk it even it it works.
>
> > On Apr 30, 2015, at 11:02 PM, Pagliari, Roberto <rpagli...@appcomsci.com>
> wrote:
> >
> > Suppose I train a classifier with dataset1, which contains labels
> >
> > 0
> > 3
> > 4
> > 6
> > 7
> >
> > and then predict over dataset2 with labels
> >
> > 0
> > 3
> > 4
> > 8
> > 10
> >
> > will the hashing be the same for labels 0, 3 and 4? and will scikit
> learn get confused by seeing new labels such as 8 and 10?
> >
> > Thank you,
> >
> >
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