index.rst

.. 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

:ref:`Classification <supervised-learning>`

Identifying to which set of categories a new observation belong to.

Applications: Spam detection, Image recognition.
Algorithms: 

:ref:`SVM<svm>`, :ref:`nearest neighbors<classification>`, :ref:`random forest<forest>`, ...

:ref:`Examples<general_examples>`

:ref:`Regression <supervised-learning>`

Predicting a continuous value for a new example.

Applications: Drug response, Stock prices.
Algorithms: 

:ref:`SVR<svm>`, :ref:`ridge regression<ridge_regression>`, :ref:`Lasso<lasso>`, ...

:ref:`Examples<general_examples>`

:ref:`Clustering<clustering>`

Automatic grouping of similar objects into sets.

Applications: Customer segmentation, Grouping experiment outcomes
Algorithms: 

:ref:`k-Means<k_means>`, :ref:`spectral clustering<spectral_clustering>`, :ref:`mean-shift<mean_shift>`, ...

:ref:`Examples<cluster_examples>`

:ref:`Dimensionality reduction<decompositions>`

Reducing the number of random variables to consider.

Applications: Visualization, Increased efficiency
Algorithms: 

:ref:`PCA<PCA>`, :ref:`feature selection<feature_selection>`, :ref:`non-negative matrix factorization<NMF>`.

:ref:`Examples<decomposition_examples>`

:ref:`Model selection<model_selection>`

Comparing, validating and choosing parameters and models.

Goal: Improved accuracy via parameter tuning
Modules: 

:ref:`grid search<grid_search>`, :ref:`cross validation<cross_validation>`, :ref:`metrics<model_evaluation>`.

:ref:`Examples<general_examples>`

:ref:`Preprocessing<preprocessing>`

Feature extraction and normalization.

Application: Transforming input data such as text for use with machine learning algorithms.
Modules: 

:ref:`preprocessing<preprocessing>`, :ref:`feature extraction<feature_extraction>`.

:ref:`Examples<general_examples>`

News

• On-going development: What's new (changelog)
• July 2014. scikit-learn 0.15.0 is available for download (Changelog).
• July 14-20th, 2014: international sprint. During this week-long sprint, we gathered 18 of the core contributors in Paris. We want to thank our sponsors: Paris-Saclay Center for Data Science & Digicosme and our hosts La Paillasse, Criteo, Inria, and tinyclues.
• August 2013. scikit-learn 0.14 is available for download (Changelog).

Community

• About us See authors # scikit-learn
• More Machine Learning Find related projects
• Questions? See stackoverflow # scikit-learn
• Mailing list: [email protected]
• IRC: #scikit-learn @ freenode

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