I agree with you.
I'm just not sure whether scikit learn would handle that or not.
thank you,
________________________________
From: Michael Eickenberg [michael.eickenb...@gmail.com]
Sent: Friday, May 01, 2015 11:13 AM
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] class label hashing
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<mailto: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<mailto:se.rasc...@gmail.com>]
Sent: Thursday, April 30, 2015 11:08 PM
To:
scikit-learn-general@lists.sourceforge.net<mailto: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<mailto: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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