8000 [MRG] DOC Replacing "the scikit" with "scikit-learn" (#10126) · maskani-moh/scikit-learn@2cafde9 · GitHub
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[MRG] DOC Replacing "the scikit" with "scikit-learn" (scikit-learn#10126)
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‎doc/datasets/index.rst

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load_breast_cancer
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These datasets are useful to quickly illustrate the behavior of the
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various algorithms implemented in the scikit. They are however often too
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various algorithms implemented in scikit-learn. They are however often too
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small to be representative of real world machine learning tasks.
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.. _sample_images:
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Sample images
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=============
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The scikit also embed a couple of sample JPEG images published under Creative
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Scikit-learn also embed a couple of sample JPEG images published under Creative
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Commons license by their authors. Those image can be useful to test algorithms
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and pipeline on 2D data.
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‎doc/developers/performance.rst

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for interactively exploring the relevant part for the code.
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Suppose we want to profile the Non Negative Matrix Factorization module
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of the scikit. Let us setup a new IPython session and load the digits
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of scikit-learn. Let us setup a new IPython session and load the digits
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dataset and as in the :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` example::
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In [1]: from sklearn.decomposition import NMF

‎doc/modules/dp-derivation.rst

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derivation of the inference algorithm and all the update and
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lower-bound equations. If you're not interested in learning how to
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derive similar algorithms yourself and you're not interested in
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changing/debugging the implementation in the scikit this document is
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changing/debugging the implementation in scikit-learn this document is
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not for you.
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The complexity of this implementation is linear in the number of

‎doc/modules/model_persistence.rst

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

‎doc/presentations.rst

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

‎doc/tutorial/basic/tutorial.rst

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

‎doc/tutorial/statistical_inference/settings.rst

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these arrays is the **samples** axis, while the second is the
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**features** axis.
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.. topic:: A simple example shipped with the scikit: iris dataset
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.. topic:: A simple example shipped with scikit-learn: iris dataset
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::
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>>> plt.imshow(digits.images[-1], cmap=plt.cm.gray_r) #doctest: +SKIP
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<matplotlib.image.AxesImage object at ...>
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To use this dataset with the scikit, we transform each 8x8 image into a
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To use this dataset with scikit-learn, we transform each 8x8 image into a
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feature vector of length 64 ::
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>>> data = digits.images.reshape((digits.images.shape[0], -1))

‎doc/tutorial/statistical_inference/unsupervised_learning.rst

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With agglomerative clustering, it is possible to specify which samples can be
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clustered together by giving a connectivity graph. Graphs in the scikit
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clustered together by giving a connectivity graph. Graphs in scikit-learn
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are represented by their adjacency matrix. Often, a sparse matrix is used.
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This can be useful, for instance, to retrieve connected regions (sometimes
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also referred to as connected components) when

‎examples/README.txt

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General examples
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----------------
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General-purpose and introductory examples for the scikit.
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General-purpose and introductory examples for scikit-learn.

‎examples/applications/wikipedia_principal_eigenvector.py

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https://en.wikipedia.org/wiki/Power_iteration
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Here the computation is achieved thanks to Martinsson's Randomized SVD
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algorithm implemented in the scikit.
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algorithm implemented in scikit-learn.
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The graph data is fetched from the DBpedia dumps. DBpedia is an extraction
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of the latent structured data of the Wikipedia content.

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