DOC fetch_20newsgroups_vectorized is based on CountVectorizer (#11685)

sklearn/datasets/descr/twenty_newsgroups.rst

@@ -117,7 +117,7 @@ components by sample in a more than 30000-dimensional space
   159.01327...
 
 :func:`sklearn.datasets.fetch_20newsgroups_vectorized` is a function which 
-returns ready-to-use tfidf features instead of file names.
+returns ready-to-use token counts features instead of file names.
 
 .. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/
 .. _`TF-IDF`: https://en.wikipedia.org/wiki/Tf-idf

sklearn/datasets/twenty_newsgroups.py

@@ -313,15 +313,20 @@ def fetch_20newsgroups(data_home=None, subset='train', categories=None,
 
 def fetch_20newsgroups_vectorized(subset="train", remove=(), data_home=None,
                                   download_if_missing=True, return_X_y=False):
-    """Load the 20 newsgroups dataset and transform it into tf-idf vectors \
+    """Load the 20 newsgroups dataset and vectorize it into token counts \
 (classification).
 
     Download it if necessary.
 
-    This is a convenience function; the tf-idf transformation is done using the
-    default settings for `sklearn.feature_extraction.text.Vectorizer`. For more
+    This is a convenience function; the transformation is done using the
+    default settings for
+    :class:`sklearn.feature_extraction.text.CountVectorizer`. For more
     advanced usage (stopword filtering, n-gram extraction, etc.), combine
-    fetch_20newsgroups with a custom `Vectorizer` or `CountVectorizer`.
+    fetch_20newsgroups with a custom
+    :class:`sklearn.feature_extraction.text.CountVectorizer`,
+    :class:`sklearn.feature_extraction.text.HashingVectorizer`,
+    :class:`sklearn.feature_extraction.text.TfidfTransformer` or
+    :class:`sklearn.feature_extraction.text.TfidfVectorizer`.
 
     =================   ==========
     Classes                     20