DOC/FIX twenty_newsgroups.rst should use TfidfVectorizer

Instead of Vectorizer. Fixes scikit-learn#2173.
smartsammler · Jul 22, 2013 · bbf74a9 · bbf74a9
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diff --git a/.mailmap b/.mailmap
@@ -27,6 +27,7 @@ Federico Vaggi <vaggi.federico@gmail.com>
 Gael Varoquaux <gael.varoquaux@normalesup.org>
 Gael Varoquaux <gael.varoquaux@normalesup.org> <varoquau@normalesup.org>
 Gilles Louppe <g.louppe@gmail.com> <g.louppe@ulg.ac.be>
+Harikrishnan S <hihari777@gmail.com>
 Hrishikesh Huilgolkar <hrishikesh911@gmail.com> <hrishikesh@QE-IND-WKS007.(none)>
 Immanuel Bayer <mane.desk@gmail.com>
 Jake VanderPlas <vanderplas@astro.washington.edu> <jakevdp@yahoo.com>

diff --git a/doc/datasets/twenty_newsgroups.rst b/doc/datasets/twenty_newsgroups.rst
@@ -86,9 +86,9 @@ for statistical analysis. This can be achieved with the utilities of the
 example that extract `TF-IDF`_ vectors of unigram tokens::
 
 
-  >>> from sklearn.feature_extraction.text import Vectorizer
+  >>> from sklearn.feature_extraction.text import TfidfVectorizer
   >>> documents = [open(f).read() for f in newsgroups_train.filenames]
-  >>> vectorizer = Vectorizer()
+  >>> vectorizer = TfidfVectorizer()
   >>> vectors = vectorizer.fit_transform(documents)
   >>> vectors.shape
   (1073, 21108)