Thanks Andy. That helped a lot.
- Adam
On Tue, May 12, 2015 at 4:50 PM, Andreas Mueller <t3k...@gmail.com> wrote:
> also the user guide:
>
> http://scikit-learn.org/dev/modules/feature_extraction.html#the-bag-of-words-representation
>
> On 05/12/2015 04:41 PM, Adam Goodkind wrote:
>
> Hi,
>
> How do I interpret the results of the TfidfVectorizer? For example:
>
> from sklearn.feature_extraction.text import TfidfVectorizer
>
> data = ['see spot run', 'run spot run']
> vectorizer = TfidfVectorizer()
> X = vectorizer.fit_transform(data)
> print(X)
>
> produces:
> (0, 0) 0.501548907094
> (0, 2) 0.501548907094
> (0, 1) 0.704909488931
> (1, 0) 0.894427191
> (1, 2) 0.4472135955
>
> I understand from the docs that this is a sparse matrix. However, I'm
> unsure how to interpret it. For example, if I wanted the tfidf score of
> each word in `data`, could I get that from the sparse matrix?
>
> Thanks,
> Adam
>
> --
> *Adam Goodkind *
> adamgoodkind.com <http://www.adamgoodkind.com>
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