Another possible strategy:
Add a new class named "random garbage" to your training set with
random text collected from wikipedia or social networks messages, or
both.
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Olivier
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Karimkhan,
Two possible naive methods that you can directly use with sklearn are:
(1) use predict_proba and check if the probability of belonging to the most
probable class (p1) is less than a threshold. Or you can use the entropy
over the probability distribution instead of p1. However, an insta
2014-09-04 15:45 GMT+02:00 Karimkhan Pathan :
> Oh okay, well I tried with predict_proba. But if query is out of domain then
> classifier uniformly divide probability to all learned domains. Like in case
> of 4 domains (0.333123570669, 0.333073654046, 0.166936800591,
> 0.166865974694)
Naive Bayes
Oh okay, well I tried with predict_proba. But if query is out of domain
then classifier uniformly divide probability to all learned domains. Like
in case of 4 domains
(0.333123570669, 0.333073654046, 0.166936800591, 0.166865974694)
On Thu, Sep 4, 2014 at 7:00 PM, Gael Varoquaux <
gael.varoqu...@n
On Thu, Sep 04, 2014 at 05:22:02PM +0530, Karimkhan Pathan wrote:
> Well could you please throw light on my classification issue? I guess
> you might be knowing well whether something helpful class/method exists
> in scikit which can solve this issue.
I don't know. I would naively try to do a pre
Hey Gaël,
Happy to see you on this thread. Actually today only I was listening to
your scikit Ipython notebook tutorial.
Well could you please throw light on my classification issue? I guess you
might be knowing well whether something helpful class/method exists in
scikit which can solve this iss
On Thu, Sep 04, 2014 at 11:01:44AM +0200, Mohamed-Rafik Bouguelia wrote:
> An example of this is the paper that can be found here: http://www.loria.fr/
> ~mbouguel/papers/BougueliaICPR.pdf
> Mohamed-Rafik Bouguelia, Yoland Belaid and Abdel Belaid. Efficient active
> novel
> class detection for dat
Hi Patrick,
Juste for information, there is some existing techniques to detect test
instances whose class is not provided for training. Instead of letting the
classifier put those instances in the closest match it can (the most
probable known class), we detect that they belongs to a novel class wh
Hi Patrick,
Yeah, you might be correct. But when I input testing query 'what is where'
with all stopwords, and having filter for stopwords still it classifies it
as :
'what is what' => films => 0.
'what is what' => laptops => 0.
'what is what' => medicine => 0.1667
Dear Sebastian,
Thanks for reply. My actual alpha value was 0.1, I changed it to 0 and
tested the code. But it behave similarly.
On Wed, Sep 3, 2014 at 8:01 PM, Sebastian Raschka
wrote:
> This is due to the Laplace smoothening. If I understand correctly, you
> want the classification to fail if
Hi Karimkhan,
If I am understanding your question correctly, you are asking to classify
test data in a class that is not specified in your training set.
For instance if you have three classes of news article specified in your
training data (e.g. politics, sports, and food) and you try to classify
This is due to the Laplace smoothening. If I understand correctly, you want the
classification to fail if there is a new feature value (e.g., a word that is
not in the vocabulary when you are doing text classification)?
You can set the alpha parameter to 0 (see
http://scikit-learn.org/stable/mo
I have trained my classifier using 20 domain datasets using MultinomialNB.
And it is working fine for these 20 domains.
Issue is, if I make query which contains text which does not belongs to any
of these 20 domain, even it gives classification result.
Is it possible that if query does not belon
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