Yes. In addition, undertested and breaks the API of the scikit. The reason is 
that it doesn't get used much as is developped only by a small fraction of the 
development team.

This is actually an example of why I don't want application-specific code in 
the scikit: such code is bound to evolve somewhat in isolation. I am not saying 
that this code should go. I am saying that I don't want it to grow much.

Gael

----- Original message -----
> Hi all,
> I spent a half hour last night trying to understand the text feature 
> extractors in sklearn.feature_extraction.text.   I frankly got nowhere: 
> it is woefully under-documented, both in doc-strings and the online 
> documentation.   Is there anybody who has a familiarity with these 
> routines and would be willing to spend some time on the docs?   That 
> would be a huge contribution to the usability of scikit-learn.   Thanks
>       Jake
> 
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------------------------------------------------------------------------------
All of the data generated in your IT infrastructure is seriously valuable.
Why? It contains a definitive record of application performance, security
threats, fraudulent activity, and more. Splunk takes this data and makes
sense of it. IT sense. And common sense.
http://p.sf.net/sfu/splunk-d2dcopy2
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