Oh yeah, sorry for mistake.
On Wed, Oct 8, 2014 at 3:12 PM, Lars Buitinck wrote:
> 2014-10-08 11:32 GMT+02:00 Karimkhan Pathan :
> > can I use this trained file with nltk to classify `plain input text`?
>
> This is the scikit-learn mailing list. You should be asking
I have 114MB sized model file which is trained using svmlight.
file looks like :
*SVM-light Version V6.020 # kernel type3 # kernel parameter
-d1 # kernel parameter -g1 # kernel parameter -s1 # kernel
parameter -rempty# kernel parameter -u69
oqu...@normalesup.org> wrote:
> 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 t
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
ord 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/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB)
>> which would disable the Laplac
inomialNB)
> which would disable the Laplace smoothening.
>
> Best,
> Sebastian Raschka
>
> > On Sep 3, 2014, at 6:20 AM, Karimkhan Pathan
> wrote:
> >
> > I have trained my classifier using 20 domain datasets using
> MultinomialNB. And it is working fine for
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
