diff --git a/doc/datasets/twenty_newsgroups.rst b/doc/datasets/twenty_newsgroups.rst
index 0f4d728f7151aaad3694c53f9fdea32dea143c9c..e82b5e4112673040594456abc3a49087a8f987bf 100644
--- a/doc/datasets/twenty_newsgroups.rst
+++ b/doc/datasets/twenty_newsgroups.rst
@@ -12,7 +12,7 @@ This module contains two loaders. The first one,
 returns a list of the raw text files that can be fed to text feature
 extractors such as :class:`sklearn.feature_extraction.text.Vectorizer`
 with custom parameters so as to extract feature vectors.
-The second one, ``sklearn.datasets.fetch_20newsgroups_tfidf``,
+The second one, ``sklearn.datasets.fetch_20newsgroups_vectorized``,
 returns ready-to-use features, i.e., it is not necessary to use a feature
 extractor.
 
@@ -98,7 +98,7 @@ zero features)::
   >>> vectors.nnz / vectors.shape[0]
   118
 
-``sklearn.datasets.fetch_20newsgroups_tfidf`` is a function which returns 
+``sklearn.datasets.fetch_20newsgroups_vectorized`` is a function which returns 
 ready-to-use tfidf features instead of file names.
 
 .. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
index 6eb51039808828cf31fe8147737049b52d83c337..4d089e14482cf2729d50272cefc57beee3063834 100644
--- a/doc/modules/classes.rst
+++ b/doc/modules/classes.rst
@@ -130,7 +130,7 @@ Loaders
    datasets.fetch_lfw_people
    datasets.load_20newsgroups
    datasets.fetch_20newsgroups
-   datasets.fetch_20newsgroups_tfidf
+   datasets.fetch_20newsgroups_vectorized
    datasets.fetch_olivetti_faces
 
 Samples generator
diff --git a/sklearn/datasets/__init__.py b/sklearn/datasets/__init__.py
index efa1015b995daf25c3e879eaf551017e98dff32c..74fb2e71084d877bbf0eafe5ede2df7ebb6aeb86 100644
--- a/sklearn/datasets/__init__.py
+++ b/sklearn/datasets/__init__.py
@@ -20,7 +20,7 @@ from .lfw import load_lfw_people
 from .lfw import fetch_lfw_pairs
 from .lfw import fetch_lfw_people
 from .twenty_newsgroups import fetch_20newsgroups
-from .twenty_newsgroups import fetch_20newsgroups_tfidf
+from .twenty_newsgroups import fetch_20newsgroups_vectorized
 from .twenty_newsgroups import load_20newsgroups
 from .mldata import fetch_mldata, mldata_filename
 from .samples_generator import make_classification
diff --git a/sklearn/datasets/tests/test_20news.py b/sklearn/datasets/tests/test_20news.py
index 22fa6256e3148af9d6bafed975c82d6bd67776cd..61d428a6a6b5080917eb1fb349819ed8c1a18ad0 100644
--- a/sklearn/datasets/tests/test_20news.py
+++ b/sklearn/datasets/tests/test_20news.py
@@ -34,18 +34,21 @@ def test_20news():
     assert_equal(entry1, entry2)
 
 
-def test_20news_tfidf():
+def test_20news_vectorized():
     # This test is slow.
     raise SkipTest
 
-    bunch = datasets.fetch_20newsgroups_tfidf(subset="train")
+    bunch = datasets.fetch_20newsgroups_vectorized(subset="train")
     assert_equal(bunch.data.shape, (11314, 107130))
     assert_equal(bunch.target.shape[0], 11314)
+    assert_equal(bunch.data.dtype, np.float64)
 
-    bunch = datasets.fetch_20newsgroups_tfidf(subset="test")
+    bunch = datasets.fetch_20newsgroups_vectorized(subset="test")
     assert_equal(bunch.data.shape, (7532, 107130))
     assert_equal(bunch.target.shape[0], 7532)
+    assert_equal(bunch.data.dtype, np.float64)
 
-    bunch = datasets.fetch_20newsgroups_tfidf(subset="all")
+    bunch = datasets.fetch_20newsgroups_vectorized(subset="all")
     assert_equal(bunch.data.shape, (11314 + 7532, 107130))
     assert_equal(bunch.target.shape[0], 11314 + 7532)
+    assert_equal(bunch.data.dtype, np.float64)
diff --git a/sklearn/datasets/twenty_newsgroups.py b/sklearn/datasets/twenty_newsgroups.py
index ab66d4f5a6cc8ffbbb7cd0d65ea58d1d69b0dcb7..ea06d74c0a75c20b96c169e7fe90a645bce00f91 100644
--- a/sklearn/datasets/twenty_newsgroups.py
+++ b/sklearn/datasets/twenty_newsgroups.py
@@ -50,8 +50,9 @@ from .base import Bunch
 from .base import load_files
 from ..utils import check_random_state, deprecated
 from ..utils.fixes import in1d
-from ..feature_extraction.text import Vectorizer
-from sklearn.externals import joblib
+from ..feature_extraction.text import CountVectorizer
+from ..preprocessing import normalize
+from ..externals import joblib
 
 
 logger = logging.getLogger(__name__)
@@ -192,7 +193,7 @@ def fetch_20newsgroups(data_home=None, subset='train', categories=None,
     return data
 
 
-def fetch_20newsgroups_tfidf(subset="train", data_home=None):
+def fetch_20newsgroups_vectorized(subset="train", data_home=None):
     """Load the 20 newsgroups dataset and transform it into tf-idf vectors
 
     This is a convenience function; the tf-idf transformation is done using the
@@ -238,11 +239,18 @@ def fetch_20newsgroups_tfidf(subset="train", data_home=None):
     if os.path.exists(target_file):
         X_train, X_test = joblib.load(target_file)
     else:
-        vectorizer = Vectorizer()
+        vectorizer = CountVectorizer(dtype=np.int16)
         X_train = vectorizer.fit_transform(data_train.data)
         X_test = vectorizer.transform(data_test.data)
         joblib.dump((X_train, X_test), target_file)
 
+    # the data is stored as int16 for compactness
+    # but normalize needs floats
+    X_train = X_train.astype(np.float64)
+    X_test = X_test.astype(np.float64)
+    normalize(X_train, copy=False)
+    normalize(X_test, copy=False)
+
     target_names = data_train.target_names
 
     if subset == "train":