.. We are putting the title as a raw HTML so that it doesn't appear in the contents .. raw:: html

scikit-learn: machine learning in Python

.. Here we are building a banner: a javascript selects randomly 4 images in the list .. only:: html .. |banner1| image:: auto_examples/svm/images/plot_oneclass_1.png :height: 140 :target: auto_examples/svm/plot_oneclass.html .. |banner2| image:: auto_examples/cluster/images/plot_ward_structured_vs_unstructured_2.png :height: 140 :target: auto_examples/cluster/plot_ward_structured_vs_unstructured.html .. |banner3| image:: auto_examples/gaussian_process/images/plot_gp_regression_1.png :height: 140 :target: auto_examples/gaussian_process/plot_gp_regression.html .. |banner4| image:: auto_examples/cluster/images/plot_lena_ward_segmentation_1.png :height: 140 :target: auto_examples/cluster/plot_lena_ward_segmentation.html .. |banner5| image:: auto_examples/svm/images/plot_svm_nonlinear_1.png :height: 140 :target: auto_examples/svm/plot_svm_nonlinear.html .. |banner6| image:: auto_examples/applications/images/plot_species_distribution_modeling_1.png :height: 140 :target: auto_examples/applications/plot_species_distribution_modeling.html .. |banner7| image:: auto_examples/gaussian_process/images/plot_gp_probabilistic_classification_after_regression_1.png :height: 140 :target: auto_examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.html .. |banner8| image:: auto_examples/ensemble/images/plot_forest_importances_faces_1.png :height: 140 :target: auto_examples/ensemble/plot_forest_importances_faces.html .. |banner9| image:: auto_examples/svm/images/plot_weighted_samples_1.png :height: 140 :target: auto_examples/svm/plot_weighted_samples.html .. |banner10| image:: auto_examples/linear_model/images/plot_sgd_weighted_samples_1.png :height: 140 :target: auto_examples/linear_model/plot_sgd_weighted_samples.html .. |banner11| image:: auto_examples/cluster/images/plot_kmeans_digits_1.png :height: 140 :target: auto_examples/cluster/plot_kmeans_digits.html .. |banner12| image:: auto_examples/decomposition/images/plot_faces_decomposition_2.png :height: 140 :target: auto_examples/decomposition/plot_faces_decomposition.html .. |banner13| image:: auto_examples/decomposition/images/plot_faces_decomposition_3.png :height: 140 :target: auto_examples/decomposition/plot_faces_decomposition.html .. |banner14| image:: auto_examples/images/plot_lda_qda_1.png :height: 140 :target: auto_examples/plot_lda_vs_qda.html .. |center-div| raw:: html |center-div| |banner1| |banner2| |banner3| |banner4| |banner5| |banner6| |banner7| |banner8| |banner9| |banner10| |banner11| |banner12| |banner13| |banner14| |end-div| .. topic:: Easy-to-use and general-purpose machine learning in Python ``scikit-learn`` is a Python module integrating classic machine learning algorithms in the tightly-knit scientific Python world (`numpy `_, `scipy `_, `matplotlib `_). It aims to provide simple and efficient solutions to learning problems, accessible to everybody and reusable in various contexts: **machine-learning as a versatile tool for science and engineering**. .. raw:: html
**License:** Open source, commercially usable: **BSD license** (3 clause) .. include:: includes/big_toc_css.rst Documentation for scikit-learn **version** |release|. For other versions and printable format, see :ref:`documentation_resources`. User Guide ========== .. toctree:: :maxdepth: 2 user_guide.rst Example Gallery =============== .. toctree:: :maxdepth: 2 auto_examples/index Development =========== .. toctree:: :maxdepth: 2 developers/index developers/performance developers/utilities developers/debugging about .. toctree:: :hidden: support whats_new