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 <[email protected]>
wrote:
> 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/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB)
> which would disable the Laplace smoothening.
>
> Best,
> Sebastian Raschka
>
> > On Sep 3, 2014, at 6:20 AM, Karimkhan Pathan <[email protected]>
> wrote:
> >
> > 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 belongs to any of 20 domain, it
> should get probability value 0?
> >
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