diff --git a/examples/text/mlcomp_sparse_document_classification.py b/examples/text/mlcomp_sparse_document_classification.py
deleted file mode 100644
index de8f94725eafde55f2d3e724415dc8d00038e6bd..0000000000000000000000000000000000000000
--- a/examples/text/mlcomp_sparse_document_classification.py
+++ /dev/null
@@ -1,145 +0,0 @@
-"""
-========================================================
-Classification of text documents: using a MLComp dataset
-========================================================
-
-This is an example showing how the scikit-learn can be used to classify
-documents by topics using a bag-of-words approach. This example uses
-a scipy.sparse matrix to store the features instead of standard numpy arrays.
-
-The dataset used in this example is the 20 newsgroups dataset and should be
-downloaded from the http://mlcomp.org (free registration required):
-
-  http://mlcomp.org/datasets/379
-
-Once downloaded unzip the archive somewhere on your filesystem.
-For instance in::
-
-  % mkdir -p ~/data/mlcomp
-  % cd  ~/data/mlcomp
-  % unzip /path/to/dataset-379-20news-18828_XXXXX.zip
-
-You should get a folder ``~/data/mlcomp/379`` with a file named ``metadata``
-and subfolders ``raw``, ``train`` and ``test`` holding the text documents
-organized by newsgroups.
-
-Then set the ``MLCOMP_DATASETS_HOME`` environment variable pointing to
-the root folder holding the uncompressed archive::
-
-  % export MLCOMP_DATASETS_HOME="~/data/mlcomp"
-
-Then you are ready to run this example using your favorite python shell::
-
-  % ipython examples/mlcomp_sparse_document_classification.py
-
-"""
-
-# Author: Olivier Grisel <olivier.grisel@ensta.org>
-# License: BSD 3 clause
-
-from __future__ import print_function
-
-from time import time
-import sys
-import os
-import numpy as np
-import scipy.sparse as sp
-import matplotlib.pyplot as plt
-
-from sklearn.datasets import load_mlcomp
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import SGDClassifier
-from sklearn.metrics import confusion_matrix
-from sklearn.metrics import classification_report
-from sklearn.naive_bayes import MultinomialNB
-
-
-print(__doc__)
-
-if 'MLCOMP_DATASETS_HOME' not in os.environ:
-    print("MLCOMP_DATASETS_HOME not set; please follow the above instructions")
-    sys.exit(0)
-
-# Load the training set
-print("Loading 20 newsgroups training set... ")
-news_train = load_mlcomp('20news-18828', 'train')
-print(news_train.DESCR)
-print("%d documents" % len(news_train.filenames))
-print("%d categories" % len(news_train.target_names))
-
-print("Extracting features from the dataset using a sparse vectorizer")
-t0 = time()
-vectorizer = TfidfVectorizer(encoding='latin1')
-X_train = vectorizer.fit_transform((open(f).read()
-                                    for f in news_train.filenames))
-print("done in %fs" % (time() - t0))
-print("n_samples: %d, n_features: %d" % X_train.shape)
-assert sp.issparse(X_train)
-y_train = news_train.target
-
-print("Loading 20 newsgroups test set... ")
-news_test = load_mlcomp('20news-18828', 'test')
-t0 = time()
-print("done in %fs" % (time() - t0))
-
-print("Predicting the labels of the test set...")
-print("%d documents" % len(news_test.filenames))
-print("%d categories" % len(news_test.target_names))
-
-print("Extracting features from the dataset using the same vectorizer")
-t0 = time()
-X_test = vectorizer.transform((open(f).read() for f in news_test.filenames))
-y_test = news_test.target
-print("done in %fs" % (time() - t0))
-print("n_samples: %d, n_features: %d" % X_test.shape)
-
-
-###############################################################################
-# Benchmark classifiers
-def benchmark(clf_class, params, name):
-    print("parameters:", params)
-    t0 = time()
-    clf = clf_class(**params).fit(X_train, y_train)
-    print("done in %fs" % (time() - t0))
-
-    if hasattr(clf, 'coef_'):
-        print("Percentage of non zeros coef: %f"
-              % (np.mean(clf.coef_ != 0) * 100))
-    print("Predicting the outcomes of the testing set")
-    t0 = time()
-    pred = clf.predict(X_test)
-    print("done in %fs" % (time() - t0))
-
-    print("Classification report on test set for classifier:")
-    print(clf)
-    print()
-    print(classification_report(y_test, pred,
-                                target_names=news_test.target_names))
-
-    cm = confusion_matrix(y_test, pred)
-    print("Confusion matrix:")
-    print(cm)
-
-    # Show confusion matrix
-    plt.matshow(cm)
-    plt.title('Confusion matrix of the %s classifier' % name)
-    plt.colorbar()
-
-
-print("Testbenching a linear classifier...")
-parameters = {
-    'loss': 'hinge',
-    'penalty': 'l2',
-    'n_iter': 50,
-    'alpha': 0.00001,
-    'fit_intercept': True,
-}
-
-benchmark(SGDClassifier, parameters, 'SGD')
-
-print("Testbenching a MultinomialNB classifier...")
-parameters = {'alpha': 0.01}
-
-benchmark(MultinomialNB, parameters, 'MultinomialNB')
-
-plt.show()
diff --git a/sklearn/datasets/mlcomp.py b/sklearn/datasets/mlcomp.py
index 545492834c18c6348690a3b52df3e6d03b566fcd..e97ab047a4fb404043e450c0da1652ae0b9f277f 100644
--- a/sklearn/datasets/mlcomp.py
+++ b/sklearn/datasets/mlcomp.py
@@ -5,6 +5,7 @@
 import os
 import numbers
 from sklearn.datasets.base import load_files
+from sklearn.utils import deprecated
 
 
 def _load_document_classification(dataset_path, metadata, set_=None, **kwargs):
@@ -19,6 +20,9 @@ LOADERS = {
 }
 
 
+@deprecated("since the http://mlcomp.org/ website will shut down "
+            "in March 2017, the load_mlcomp function was deprecated "
+            "in version 0.19 and will be removed in 0.21.")
 def load_mlcomp(name_or_id, set_="raw", mlcomp_root=None, **kwargs):
     """Load a datasets as downloaded from http://mlcomp.org