scikit-learn/doc/index.rst

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.. We want the previous/next button to work on the user guide pages and on the
API. We have to put the doctree so that sphinx populates the
rellinks. Yet, we don't want it to be displayed on the main page, hence we
don't display through the css.
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.. toctree::
tutorial/index
user_guide
auto_examples/index
faq
support
whats_new
presentations
about
documentation
datasets/index
datasets/covtype
datasets/labeled_faces
datasets/mldata
datasets/olivetti_faces
datasets/twenty_newsgroups
modules/classes
testimonials/testimonials
developers/index
developers/debugging
developers/maintainer
developers/performance
developers/utilities
install
tutorial/basic/tutorial
tutorial/machine_learning_map/index
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.. This is were the real work starts.
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<!-- Block section -->
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<!-- Classification -->
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<h2 >
:ref:`Classification <supervised-learning>`
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<blockquote>
<p>Identifying to which category an object belongs to.</p>
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<strong>Applications</strong>: Spam detection, Image recognition.</br>
<strong>Algorithms</strong>:&nbsp;
:ref:`SVM<svm>`, :ref:`nearest neighbors<classification>`, :ref:`random forest<forest>`, ...
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:ref:`Examples<general_examples>`
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:ref:`Regression <supervised-learning>`
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<p>Predicting a continuous-valued attribute associated with an object.</p>
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<strong>Applications</strong>: Drug response, Stock prices.</br>
<strong>Algorithms</strong>:&nbsp;
:ref:`SVR<svm>`, :ref:`ridge regression<ridge_regression>`, :ref:`Lasso<lasso>`, ...
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:ref:`Examples<general_examples>`
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:ref:`Clustering<clustering>`
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<p>Automatic grouping of similar objects into sets.</p>
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<strong>Applications</strong>: Customer segmentation, Grouping experiment outcomes</br>
<strong>Algorithms</strong>:&nbsp;
:ref:`k-Means<k_means>`, :ref:`spectral clustering<spectral_clustering>`, :ref:`mean-shift<mean_shift>`, ...
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:ref:`Examples<cluster_examples>`
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:ref:`Dimensionality reduction<decompositions>`
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<p>Reducing the number of random variables to consider.</p>
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<strong>Applications</strong>: Visualization, Increased efficiency</br>
<strong>Algorithms</strong>:&nbsp;
:ref:`PCA<PCA>`, :ref:`feature selection<feature_selection>`, :ref:`non-negative matrix factorization<NMF>`.
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:ref:`Examples<decomposition_examples>`
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:ref:`Model selection<model_selection>`
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<p>Comparing, validating and choosing parameters and models.</p>
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<strong>Goal</strong>: Improved accuracy via parameter tuning</br>
<strong>Modules</strong>:&nbsp;
:ref:`grid search<grid_search>`, :ref:`cross validation<cross_validation>`, :ref:`metrics<model_evaluation>`.
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:ref:`Examples<general_examples>`
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:ref:`Preprocessing<preprocessing>`
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<blockquote>
<p>Feature extraction and normalization.</p>
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<strong>Application</strong>: Transforming input data such as text for use with machine learning algorithms.</br>
<strong>Modules</strong>:&nbsp;
:ref:`preprocessing<preprocessing>`, :ref:`feature extraction<feature_extraction>`.
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:ref:`Examples<general_examples>`
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</div> <!-- container -->
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<h4>News</h4>
<ul>
<li><em>On-going development:</em>
<a href="whats_new.html"><em>What's new</em> (changelog)</a>
</li>
<li><em>July 2014.</em> scikit-learn 0.15.0 is available for download (<a href="whats_new.html">Changelog</a>).
</li>
<li><em>July 14-20th, 2014: international sprint.</em>
During this week-long sprint, we gathered 18 of the core
contributors in Paris.
We want to thank our sponsors:
<a href="http://www.campus-paris-saclay.fr/en/Idex-Paris-Saclay/Les-Lidex/Paris-Saclay-Center-for-Data-Science">
Paris-Saclay Center for Data Science</a>
& <a href="https://digicosme.lri.fr">Digicosme</a> and our
hosts <a href="http://lapaillasse.org">La Paillasse</a>,
<a href="http://www.criteo.com/">Criteo</a>,
<a href="http://www.inria.fr/">Inria</a>,
and <a href="http://www.tinyclues.com/">tinyclues</a>.
</li>
<li><em>August 2013.</em> scikit-learn 0.14 is available for download (<a href="whats_new.html">Changelog</a>).
</li>
</ul>
</div>
<!-- Community -->
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<h4>Community</h4>
<ul>
<li><em>About us</em> See <a href="about.html">authors</a> # scikit-learn</li>
<li><em>More Machine Learning</em> Find <a href="related_projects.html">related projects</a></li>
<li><em>Questions?</em> See <a href="http://stackoverflow.com/questions/tagged/scikit-learn">stackoverflow</a> # scikit-learn</li>
<li><em>Mailing list:</em> <a href="https://lists.sourceforge.net/lists/listinfo/scikit-learn-general">scikit-learn-general@lists.sourceforge.net</a></li>
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<h4>Who uses scikit-learn?</h4>
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<em>"scikit-learn makes doing advanced analysis in Python accessible to anyone."</em>
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