scikit-learn · lesteve · Nov 14, 2017 · Nov 13, 2017 · Nov 14, 2017 · Nov 14, 2017
diff --git a/doc/datasets/index.rst b/doc/datasets/index.rst
@@ -64,15 +64,15 @@ require to download any file from some external website.
    load_breast_cancer
 
 These datasets are useful to quickly illustrate the behavior of the
-various algorithms implemented in the scikit. They are however often too
+various algorithms implemented in scikit-learn. They are however often too
 small to be representative of real world machine learning tasks.
 
 .. _sample_images:
 
 Sample images
 =============
 
-The scikit also embed a couple of sample JPEG images published under Creative
+Scikit-learn also embed a couple of sample JPEG images published under Creative
 Commons license by their authors. Those image can be useful to test algorithms
 and pipeline on 2D data.
 

diff --git a/doc/developers/performance.rst b/doc/developers/performance.rst
@@ -94,7 +94,7 @@ loads and prepare you data and then use the IPython integrated profiler
 for interactively exploring the relevant part for the code.
 
 Suppose we want to profile the Non Negative Matrix Factorization module
-of the scikit. Let us setup a new IPython session and load the digits
+of scikit-learn. Let us setup a new IPython session and load the digits
 dataset and as in the :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` example::
 
   In [1]: from sklearn.decomposition import NMF

diff --git a/doc/modules/dp-derivation.rst b/doc/modules/dp-derivation.rst
@@ -23,7 +23,7 @@ complex, or even more. For this reason we present here a full
 derivation of the inference algorithm and all the update and
 lower-bound equations. If you're not interested in learning how to
 derive similar algorithms yourself and you're not interested in
-changing/debugging the implementation in the scikit this document is
+changing/debugging the implementation in scikit-learn this document is
 not for you.
 
 The complexity of this implementation is linear in the number of

diff --git a/doc/modules/model_persistence.rst b/doc/modules/model_persistence.rst
@@ -13,7 +13,7 @@ security and maintainability issues when working with pickle serialization.
 Persistence example
 -------------------
 
-It is possible to save a model in the scikit by using Python's built-in
+It is possible to save a model in scikit-learn by using Python's built-in
 persistence model, namely `pickle <https://docs.python.org/2/library/pickle.html>`_::
 
   >>> from sklearn import svm
@@ -35,7 +35,7 @@ persistence model, namely `pickle <https://docs.python.org/2/library/pickle.html
   >>> y[0]
   0
 
-In the specific case of the scikit, it may be more interesting to use
+In the specific case of scikit-learn, it may be more interesting to use
 joblib's replacement of pickle (``joblib.dump`` & ``joblib.load``),
 which is more efficient on objects that carry large numpy arrays internally as
 is often the case for fitted scikit-learn estimators, but can only pickle to the

diff --git a/doc/presentations.rst b/doc/presentations.rst
@@ -37,7 +37,7 @@ Videos
   <http://videolectures.net/icml2010_varaquaux_scik/>`_ by `Gael Varoquaux`_ at
   ICML 2010
 
-    A three minute video from a very early stage of the scikit, explaining the
+    A three minute video from a very early stage of scikit-learn, explaining the
     basic idea and approach we are following.
 
 - `Introduction to statistical learning with scikit-learn <http://archive.org/search.php?query=scikit-learn>`_

diff --git a/doc/tutorial/basic/tutorial.rst b/doc/tutorial/basic/tutorial.rst
@@ -209,7 +209,7 @@ example that you can run and study:
 Model persistence
 -----------------
 
-It is possible to save a model in the scikit by using Python's built-in
+It is possible to save a model in scikit-learn by using Python's built-in
 persistence model, namely `pickle <https://docs.python.org/2/library/pickle.html>`_::
 
   >>> from sklearn import svm
@@ -231,7 +231,7 @@ persistence model, namely `pickle <https://docs.python.org/2/library/pickle.html
   >>> y[0]
   0
 
-In the specific case of the scikit, it may be more interesting to use
+In the specific case of scikit-learn, it may be more interesting to use
 joblib's replacement of pickle (``joblib.dump`` & ``joblib.load``),
 which is more efficient on big data, but can only pickle to the disk
 and not to a string::

diff --git a/doc/tutorial/statistical_inference/settings.rst b/doc/tutorial/statistical_inference/settings.rst
@@ -12,7 +12,7 @@ list of multi-dimensional observations. We say that the first axis of
 these arrays is the **samples** axis, while the second is the
 **features** axis.
 
-.. topic:: A simple example shipped with the scikit: iris dataset
+.. topic:: A simple example shipped with scikit-learn: iris dataset
 
     ::
 
@@ -46,7 +46,7 @@ needs to be preprocessed in order to be used by scikit-learn.
         >>> plt.imshow(digits.images[-1], cmap=plt.cm.gray_r) #doctest: +SKIP
         <matplotlib.image.AxesImage object at ...>
 
-    To use this dataset with the scikit, we transform each 8x8 image into a
+    To use this dataset with scikit-learn, we transform each 8x8 image into a
     feature vector of length 64 ::
 
         >>> data = digits.images.reshape((digits.images.shape[0], -1))

diff --git a/doc/tutorial/statistical_inference/unsupervised_learning.rst b/doc/tutorial/statistical_inference/unsupervised_learning.rst
@@ -171,7 +171,7 @@ Connectivity-constrained clustering
 .....................................
 
 With agglomerative clustering, it is possible to specify which samples can be
-clustered together by giving a connectivity graph. Graphs in the scikit
+clustered together by giving a connectivity graph. Graphs in scikit-learn
 are represented by their adjacency matrix. Often, a sparse matrix is used.
 This can be useful, for instance, to retrieve connected regions (sometimes
 also referred to as connected components) when

diff --git a/examples/README.txt b/examples/README.txt
@@ -3,4 +3,4 @@
 General examples
 ----------------
 
-General-purpose and introductory examples for the scikit.
+General-purpose and introductory examples for scikit-learn.
diff --git a/examples/applications/wikipedia_principal_eigenvector.py b/examples/applications/wikipedia_principal_eigenvector.py
@@ -23,7 +23,7 @@
     https://en.wikipedia.org/wiki/Power_iteration
 
 Here the computation is achieved thanks to Martinsson's Randomized SVD
-algorithm implemented in the scikit.
+algorithm implemented in scikit-learn.
 
 The graph data is fetched from the DBpedia dumps. DBpedia is an extraction
 of the latent structured data of the Wikipedia content.

diff --git a/sklearn/__check_build/__init__.py b/sklearn/__check_build/__init__.py
@@ -1,5 +1,5 @@
 """ Module to give helpful messages to the user that did not
-compile the scikit properly.
+compile scikit-learn properly.
 """
 import os
 

diff --git a/sklearn/__init__.py b/sklearn/__init__.py
@@ -127,7 +127,7 @@ def config_context(**new_config):
 
 if __SKLEARN_SETUP__:
     sys.stderr.write('Partial import of sklearn during the build process.\n')
-    # We are not importing the rest of the scikit during the build
+    # We are not importing the rest of scikit-learn during the build
     # process, as it may not be compiled yet
 else:
     from . import __check_build

diff --git a/sklearn/preprocessing/label.py b/sklearn/preprocessing/label.py
@@ -160,7 +160,7 @@ class LabelBinarizer(BaseEstimator, TransformerMixin):
     """Binarize labels in a one-vs-all fashion
 
     Several regression and binary classification algorithms are
-    available in the scikit. A simple way to extend these algorithms
+    available in scikit-learn. A simple way to extend these algorithms
     to the multi-class classification case is to use the so-called
     one-vs-all scheme.
 
@@ -393,7 +393,7 @@ def label_binarize(y, classes, neg_label=0, pos_label=1, sparse_output=False):
     """Binarize labels in a one-vs-all fashion
 
     Several regression and binary classification algorithms are
-    available in the scikit. A simple way to extend these algorithms
+    available in scikit-learn. A simple way to extend these algorithms
     to the multi-class classification case is to use the so-called
     one-vs-all scheme.
 

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -77,7 +77,7 @@ def test_non_meta_estimators():
 
 def test_configure():
     # Smoke test the 'configure' step of setup, this tests all the
-    # 'configure' functions in the setup.pys in the scikit
+    # 'configure' functions in the setup.pys in scikit-learn
     cwd = os.getcwd()
     setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], '..'))
     setup_filename = os.path.join(setup_path, 'setup.py')