> But when I test the prediction for a new sentence or text, it gives wrong
prediction.
How do you measure that ?
Having a few badly classified instances does not necessarily means the
learning has failed.
A good classification accuracy for text classification is typically > 80%,
what is yours ?
Also, HashingVectorizer is not really involved in classification accuracy
here - IMHO.
The main factor would probably be how close your new examples are to the
training set. E.g. in the out-of-core example we keep the first 1000
instances for testing. If you just ask predictions for texts taken from
other sources the classification would probably be worse...
HTH
Eustache
2014-07-24 4:35 GMT+02:00 Kartik Kumar Perisetla <kartik.p...@gmail.com>:
> Hello,
>
> I am creating a content classifier using scikit-learn through
> HashingVectorizer( using this as reference:
> http://scikit-learn.org/dev/auto_examples/applications/plot_out_of_core_classification.html
> ).
>
> The training dataset I am using wikipedia. For example, for "management"
> category I am training it with few articles related to management. i.e.
> Entire article related to management is one training instance.
>
> I did training with 50 categories and total of ~4000 training instances.
> But when I test the prediction for a new sentence or text, it gives wrong
> prediction.
>
> So my question is do I need to break wikipedia article into sentences and
> label each sentence with category name to make it work correctly? Since I
> am using HashingVectorizer, my intuition is it is creating a hash for
> entire training instance and not for tokens in it. Is it true?
>
> Also, Could someone please throw some light on how HashingVectorizer works?
>
> Thanks,
> Kartik
>
> --
> Regards,
>
> Kartik Perisetla
>
>
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