It should. If not, please report a bug.
On 05/01/2015 11:16 AM, Pagliari, Roberto wrote:
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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