Thanks, Lars, that's what I thought (natural log). I will try some more 
combinations later and browse through the source code to see if I can somehow 
manage to reproduce the results. Maybe it would be good to write it up as an 
example then for the documentation -- in case someone else is wondering about 
it since it is slightly different from the "classic" tf-idf approach.

Btw. is there anything that speaks against those negative values in the feature 
vectors? I mean for e.g., SGD classifiers it can maybe be beneficial to have 
values that can be positive and negative.

Best,
Sebastian


> On May 22, 2015, at 12:00 PM, Lars Buitinck <larsm...@gmail.com> wrote:
> 
> 2015-05-22 8:29 GMT+02:00 Sebastian Raschka <se.rasc...@gmail.com>:
>> The default equation is:
>> # idf = log ( number_of_docs / number_of_docs_where_term_appears )
>> 
>> And in the online documentation at
>> http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html
>> I found the additional info:
>>> smooth_idf : boolean, default=True
>>> Smooth idf weights by adding one to document frequencies, as if an extra 
>>> document was seen containing every term in the collection exactly once. 
>>> Prevents zero divisions.
>> 
>> 
>> So that I assume that the smooth_idf is calculated as follows:
>> # smooth_idf = log ( number_of_docs / (1 + 
>> number_of_docs_where_term_appears) )
> 
> I don't have a full answer ready, but note that number_of_docs must
> also be incremented by the smoothing term (which is actually a
> misnomer, IIRC). Otherwise the logs can come out negative.
> 
> Logs are also always natural logs in scikit-learn.
> 
> HTH
> 
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