:orphan:



.. _sphx_glr_auto_examples:

.. _general_examples:

Examples
========


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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_release_highlights:

.. _release_highlights_examples:

Release Highlights
------------------

These examples illustrate the main features of the releases of scikit-learn.



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    <div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.23! Many bug fixes and improvements we...">

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 .. figure:: /auto_examples/release_highlights/images/thumb/sphx_glr_plot_release_highlights_0_23_0_thumb.png
     :alt: Release Highlights for scikit-learn 0.23

     :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_23_0.py`

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    </div>


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   /auto_examples/release_highlights/plot_release_highlights_0_23_0

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    <div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes an...">

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 .. figure:: /auto_examples/release_highlights/images/thumb/sphx_glr_plot_release_highlights_0_22_0_thumb.png
     :alt: Release Highlights for scikit-learn 0.22

     :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_22_0.py`

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    </div>


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   /auto_examples/release_highlights/plot_release_highlights_0_22_0
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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_bicluster:

.. _bicluster_examples:

Biclustering
------------

Examples concerning the :mod:`sklearn.cluster.bicluster` module.



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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clus...">

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 .. figure:: /auto_examples/bicluster/images/thumb/sphx_glr_plot_spectral_coclustering_thumb.png
     :alt: A demo of the Spectral Co-Clustering algorithm

     :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py`

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    </div>


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   /auto_examples/bicluster/plot_spectral_coclustering

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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spe...">

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 .. figure:: /auto_examples/bicluster/images/thumb/sphx_glr_plot_spectral_biclustering_thumb.png
     :alt: A demo of the Spectral Biclustering algorithm

     :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_biclustering.py`

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    </div>


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   /auto_examples/bicluster/plot_spectral_biclustering

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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset...">

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 .. figure:: /auto_examples/bicluster/images/thumb/sphx_glr_plot_bicluster_newsgroups_thumb.png
     :alt: Biclustering documents with the Spectral Co-clustering algorithm

     :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py`

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    </div>


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   /auto_examples/bicluster/plot_bicluster_newsgroups
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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_calibration:

.. _calibration_examples:

Calibration
-----------------------

Examples illustrating the calibration of predicted probabilities of classifiers.



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    <div class="sphx-glr-thumbcontainer" tooltip="Well calibrated classifiers are probabilistic classifiers for which the output of the predict_p...">

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 .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_compare_calibration_thumb.png
     :alt: Comparison of Calibration of Classifiers

     :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py`

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    </div>


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   /auto_examples/calibration/plot_compare_calibration

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    <div class="sphx-glr-thumbcontainer" tooltip="When performing classification one often wants to predict not only the class label, but also th...">

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 .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_calibration_curve_thumb.png
     :alt: Probability Calibration curves

     :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`

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    </div>


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   /auto_examples/calibration/plot_calibration_curve

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    <div class="sphx-glr-thumbcontainer" tooltip="When performing classification you often want to predict not only the class label, but also the...">

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 .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_calibration_thumb.png
     :alt: Probability calibration of classifiers

     :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`

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    </div>


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   /auto_examples/calibration/plot_calibration

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    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class ...">

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 .. figure:: /auto_examples/calibration/images/thumb/sphx_glr_plot_calibration_multiclass_thumb.png
     :alt: Probability Calibration for 3-class classification

     :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py`

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    </div>


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   /auto_examples/calibration/plot_calibration_multiclass
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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_classification:

.. _classification_examples:

Classification
-----------------------

General examples about classification algorithms.



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    <div class="sphx-glr-thumbcontainer" tooltip="Shows how shrinkage improves classification.">

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 .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_lda_thumb.png
     :alt: Normal and Shrinkage Linear Discriminant Analysis for classification

     :ref:`sphx_glr_auto_examples_classification_plot_lda.py`

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    </div>


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   /auto_examples/classification/plot_lda

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    <div class="sphx-glr-thumbcontainer" tooltip="An example showing how the scikit-learn can be used to recognize images of hand-written digits.">

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 .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_digits_classification_thumb.png
     :alt: Recognizing hand-written digits

     :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py`

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    </div>


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   /auto_examples/classification/plot_digits_classification

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    <div class="sphx-glr-thumbcontainer" tooltip="Plot the classification probability for different classifiers. We use a 3 class dataset, and we...">

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 .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_classification_probability_thumb.png
     :alt: Plot classification probability

     :ref:`sphx_glr_auto_examples_classification_plot_classification_probability.py`

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    </div>


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   /auto_examples/classification/plot_classification_probability

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    <div class="sphx-glr-thumbcontainer" tooltip="A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this ...">

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 .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_classifier_comparison_thumb.png
     :alt: Classifier comparison

     :ref:`sphx_glr_auto_examples_classification_plot_classifier_comparison.py`

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    </div>


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   /auto_examples/classification/plot_classifier_comparison

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    <div class="sphx-glr-thumbcontainer" tooltip="This example plots the covariance ellipsoids of each class and decision boundary learned by LDA...">

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 .. figure:: /auto_examples/classification/images/thumb/sphx_glr_plot_lda_qda_thumb.png
     :alt: Linear and Quadratic Discriminant Analysis with covariance ellipsoid

     :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py`

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   /auto_examples/classification/plot_lda_qda
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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_cluster:

.. _cluster_examples:

Clustering
----------

Examples concerning the :mod:`sklearn.cluster` module.



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    <div class="sphx-glr-thumbcontainer" tooltip="Plot Hierarchical Clustering Dendrogram">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_agglomerative_dendrogram_thumb.png
     :alt: Plot Hierarchical Clustering Dendrogram

     :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py`

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    </div>


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   /auto_examples/cluster/plot_agglomerative_dendrogram

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    <div class="sphx-glr-thumbcontainer" tooltip="These images how similar features are merged together using feature agglomeration.">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_digits_agglomeration_thumb.png
     :alt: Feature agglomeration

     :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py`

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    </div>


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   /auto_examples/cluster/plot_digits_agglomeration

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    <div class="sphx-glr-thumbcontainer" tooltip="Reference:">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_mean_shift_thumb.png
     :alt: A demo of the mean-shift clustering algorithm

     :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`

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    </div>


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   /auto_examples/cluster/plot_mean_shift

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    <div class="sphx-glr-thumbcontainer" tooltip="This example is meant to illustrate situations where k-means will produce unintuitive and possi...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_assumptions_thumb.png
     :alt: Demonstration of k-means assumptions

     :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`

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    </div>


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   /auto_examples/cluster/plot_kmeans_assumptions

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    <div class="sphx-glr-thumbcontainer" tooltip="This example uses a large dataset of faces to learn a set of 20 x 20 images patches that consti...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_dict_face_patches_thumb.png
     :alt: Online learning of a dictionary of parts of faces

     :ref:`sphx_glr_auto_examples_cluster_plot_dict_face_patches.py`

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    </div>


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   /auto_examples/cluster/plot_dict_face_patches

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    <div class="sphx-glr-thumbcontainer" tooltip="Face, a 1024 x 768 size image of a raccoon face, is used here to illustrate how k-means is used...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_face_compress_thumb.png
     :alt: Vector Quantization Example

     :ref:`sphx_glr_auto_examples_cluster_plot_face_compress.py`

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    </div>


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   /auto_examples/cluster/plot_face_compress

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    <div class="sphx-glr-thumbcontainer" tooltip="Reference: Brendan J. Frey and Delbert Dueck, &quot;Clustering by Passing Messages Between Data Poin...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_affinity_propagation_thumb.png
     :alt: Demo of affinity propagation clustering algorithm

     :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`

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    </div>


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   /auto_examples/cluster/plot_affinity_propagation

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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows the effect of imposing a connectivity graph to capture local structure in th...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_agglomerative_clustering_thumb.png
     :alt: Agglomerative clustering with and without structure

     :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py`

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    </div>


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   /auto_examples/cluster/plot_agglomerative_clustering

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    <div class="sphx-glr-thumbcontainer" tooltip="An illustration of various linkage option for agglomerative clustering on a 2D embedding of the...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_digits_linkage_thumb.png
     :alt: Various Agglomerative Clustering on a 2D embedding of digits

     :ref:`sphx_glr_auto_examples_cluster_plot_digits_linkage.py`

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    </div>


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   /auto_examples/cluster/plot_digits_linkage

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    <div class="sphx-glr-thumbcontainer" tooltip="This example uses spectral_clustering on a graph created from voxel-to-voxel difference on an i...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_coin_segmentation_thumb.png
     :alt: Segmenting the picture of greek coins in regions

     :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py`

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    </div>


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   :hidden:

   /auto_examples/cluster/plot_coin_segmentation

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    <div class="sphx-glr-thumbcontainer" tooltip="The plots display firstly what a K-means algorithm would yield using three clusters. It is then...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_cluster_iris_thumb.png
     :alt: K-means Clustering

     :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py`

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    </div>


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   :hidden:

   /auto_examples/cluster/plot_cluster_iris

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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, an image with connected circles is generated and spectral clustering is used t...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_segmentation_toy_thumb.png
     :alt: Spectral clustering for image segmentation

     :ref:`sphx_glr_auto_examples_cluster_plot_segmentation_toy.py`

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    </div>


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   /auto_examples/cluster/plot_segmentation_toy

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    <div class="sphx-glr-thumbcontainer" tooltip="Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spa...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_coin_ward_segmentation_thumb.png
     :alt: A demo of structured Ward hierarchical clustering on an image of coins

     :ref:`sphx_glr_auto_examples_cluster_plot_coin_ward_segmentation.py`

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    </div>


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   /auto_examples/cluster/plot_coin_ward_segmentation

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    <div class="sphx-glr-thumbcontainer" tooltip="Finds core samples of high density and expands clusters from them.">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_dbscan_thumb.png
     :alt: Demo of DBSCAN clustering algorithm

     :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`

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    </div>


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   :hidden:

   /auto_examples/cluster/plot_dbscan

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    <div class="sphx-glr-thumbcontainer" tooltip="Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reduci...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_color_quantization_thumb.png
     :alt: Color Quantization using K-Means

     :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`

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    </div>


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   /auto_examples/cluster/plot_color_quantization

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    <div class="sphx-glr-thumbcontainer" tooltip="Example builds a swiss roll dataset and runs hierarchical clustering on their position.">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png
     :alt: Hierarchical clustering: structured vs unstructured ward

     :ref:`sphx_glr_auto_examples_cluster_plot_ward_structured_vs_unstructured.py`

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    </div>


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   /auto_examples/cluster/plot_ward_structured_vs_unstructured

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    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the effect of different metrics on the hierarchical clustering.">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png
     :alt: Agglomerative clustering with different metrics

     :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py`

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    </div>


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   /auto_examples/cluster/plot_agglomerative_clustering_metrics

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    <div class="sphx-glr-thumbcontainer" tooltip="Clustering can be expensive, especially when our dataset contains millions of datapoints. Many ...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_inductive_clustering_thumb.png
     :alt: Inductive Clustering

     :ref:`sphx_glr_auto_examples_cluster_plot_inductive_clustering.py`

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    </div>


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   /auto_examples/cluster/plot_inductive_clustering

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    <div class="sphx-glr-thumbcontainer" tooltip="Demo of OPTICS clustering algorithm">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_optics_thumb.png
     :alt: Demo of OPTICS clustering algorithm

     :ref:`sphx_glr_auto_examples_cluster_plot_optics.py`

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    </div>


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   /auto_examples/cluster/plot_optics

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    <div class="sphx-glr-thumbcontainer" tooltip="This example compares the timing of Birch (with and without the global clustering step) and Min...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png
     :alt: Compare BIRCH and MiniBatchKMeans

     :ref:`sphx_glr_auto_examples_cluster_plot_birch_vs_minibatchkmeans.py`

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    </div>


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   /auto_examples/cluster/plot_birch_vs_minibatchkmeans

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    <div class="sphx-glr-thumbcontainer" tooltip="Evaluate the ability of k-means initializations strategies to make the algorithm convergence ro...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png
     :alt: Empirical evaluation of the impact of k-means initialization

     :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`

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    </div>


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   :hidden:

   /auto_examples/cluster/plot_kmeans_stability_low_dim_dense

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    <div class="sphx-glr-thumbcontainer" tooltip="The following plots demonstrate the impact of the number of clusters and number of samples on v...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_adjusted_for_chance_measures_thumb.png
     :alt: Adjustment for chance in clustering performance evaluation

     :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/cluster/plot_adjusted_for_chance_measures

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    <div class="sphx-glr-thumbcontainer" tooltip="We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_mini_batch_kmeans_thumb.png
     :alt: Comparison of the K-Means and MiniBatchKMeans clustering algorithms

     :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/cluster/plot_mini_batch_kmeans

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    <div class="sphx-glr-thumbcontainer" tooltip="This example compares 2 dimensionality reduction strategies:">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png
     :alt: Feature agglomeration vs. univariate selection

     :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection

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    <div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the various initialization strategies for K-means in terms of runtim...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_digits_thumb.png
     :alt: A demo of K-Means clustering on the handwritten digits data

     :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`

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    </div>


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   :hidden:

   /auto_examples/cluster/plot_kmeans_digits

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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different linkage methods for hierarchical clustering on ...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_linkage_comparison_thumb.png
     :alt: Comparing different hierarchical linkage methods on toy datasets

     :ref:`sphx_glr_auto_examples_cluster_plot_linkage_comparison.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/cluster/plot_linkage_comparison

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    <div class="sphx-glr-thumbcontainer" tooltip="Silhouette analysis can be used to study the separation distance between the resulting clusters...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png
     :alt: Selecting the number of clusters with silhouette analysis on KMeans clustering

     :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`

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    </div>


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   /auto_examples/cluster/plot_kmeans_silhouette_analysis

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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are &quot;int...">

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 .. figure:: /auto_examples/cluster/images/thumb/sphx_glr_plot_cluster_comparison_thumb.png
     :alt: Comparing different clustering algorithms on toy datasets

     :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/cluster/plot_cluster_comparison
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    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_covariance:

.. _covariance_examples:

Covariance estimation
---------------------

Examples concerning the :mod:`sklearn.covariance` module.



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    <div class="sphx-glr-thumbcontainer" tooltip="The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and...">

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 .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_lw_vs_oas_thumb.png
     :alt: Ledoit-Wolf vs OAS estimation

     :ref:`sphx_glr_auto_examples_covariance_plot_lw_vs_oas.py`

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    </div>


.. toctree::
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   /auto_examples/covariance/plot_lw_vs_oas

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    <div class="sphx-glr-thumbcontainer" tooltip="Using the GraphicalLasso estimator to learn a covariance and sparse precision from a small numb...">

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 .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_sparse_cov_thumb.png
     :alt: Sparse inverse covariance estimation

     :ref:`sphx_glr_auto_examples_covariance_plot_sparse_cov.py`

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    </div>


.. toctree::
   :hidden:

   /auto_examples/covariance/plot_sparse_cov

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="When working with covariance estimation, the usual approach is to use a maximum likelihood esti...">

.. only:: html

 .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_covariance_estimation_thumb.png
     :alt: Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

     :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/covariance/plot_covariance_estimation

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example to show covariance estimation with the Mahalanobis distances on Gaussian distributed...">

.. only:: html

 .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_mahalanobis_distances_thumb.png
     :alt: Robust covariance estimation and Mahalanobis distances relevance

     :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/covariance/plot_mahalanobis_distances

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers ...">

.. only:: html

 .. figure:: /auto_examples/covariance/images/thumb/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png
     :alt: Robust vs Empirical covariance estimate

     :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/covariance/plot_robust_vs_empirical_covariance
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_cross_decomposition:

.. _cross_decomposition_examples:

Cross decomposition
-------------------

Examples concerning the :mod:`sklearn.cross_decomposition` module.




.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with mu...">

.. only:: html

 .. figure:: /auto_examples/cross_decomposition/images/thumb/sphx_glr_plot_compare_cross_decomposition_thumb.png
     :alt: Compare cross decomposition methods

     :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/cross_decomposition/plot_compare_cross_decomposition
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_datasets:

.. _dataset_examples:

Dataset examples
-----------------------

Examples concerning the :mod:`sklearn.datasets` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This dataset is made up of 1797 8x8 images. Each image, like the one shown below, is of a hand-...">

.. only:: html

 .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_digits_last_image_thumb.png
     :alt: The Digit Dataset

     :ref:`sphx_glr_auto_examples_datasets_plot_digits_last_image.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/datasets/plot_digits_last_image

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and P...">

.. only:: html

 .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_iris_dataset_thumb.png
     :alt: The Iris Dataset

     :ref:`sphx_glr_auto_examples_datasets_plot_iris_dataset.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/datasets/plot_iris_dataset

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example plots several randomly generated classification datasets. For easy visualization, ...">

.. only:: html

 .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_random_dataset_thumb.png
     :alt: Plot randomly generated classification dataset

     :ref:`sphx_glr_auto_examples_datasets_plot_random_dataset.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/datasets/plot_random_dataset

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This illustrates the make_multilabel_classification dataset generator. Each sample consists of ...">

.. only:: html

 .. figure:: /auto_examples/datasets/images/thumb/sphx_glr_plot_random_multilabel_dataset_thumb.png
     :alt: Plot randomly generated multilabel dataset

     :ref:`sphx_glr_auto_examples_datasets_plot_random_multilabel_dataset.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/datasets/plot_random_multilabel_dataset
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_tree:

.. _tree_examples:

Decision Trees
--------------

Examples concerning the :mod:`sklearn.tree` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A 1D regression with decision tree.">

.. only:: html

 .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_tree_regression_thumb.png
     :alt: Decision Tree Regression

     :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/tree/plot_tree_regression

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example to illustrate multi-output regression with decision tree.">

.. only:: html

 .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_tree_regression_multioutput_thumb.png
     :alt: Multi-output Decision Tree Regression

     :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/tree/plot_tree_regression_multioutput

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surface of a decision tree trained on pairs of features of the iris dataset.">

.. only:: html

 .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_iris_dtc_thumb.png
     :alt: Plot the decision surface of a decision tree on the iris dataset

     :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/tree/plot_iris_dtc

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to preven...">

.. only:: html

 .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_cost_complexity_pruning_thumb.png
     :alt: Post pruning decision trees with cost complexity pruning

     :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/tree/plot_cost_complexity_pruning

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The decision tree structure can be analysed to gain further insight on the relation between the...">

.. only:: html

 .. figure:: /auto_examples/tree/images/thumb/sphx_glr_plot_unveil_tree_structure_thumb.png
     :alt: Understanding the decision tree structure

     :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/tree/plot_unveil_tree_structure
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_decomposition:

.. _decomposition_examples:

Decomposition
-------------

Examples concerning the :mod:`sklearn.decomposition` module.




.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A plot that compares the various Beta-divergence loss functions supported by the Multiplicative...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_beta_divergence_thumb.png
     :alt: Beta-divergence loss functions

     :ref:`sphx_glr_auto_examples_decomposition_plot_beta_divergence.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_beta_divergence

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Principal Component Analysis applied to the Iris dataset.">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_iris_thumb.png
     :alt: PCA example with Iris Data-set

     :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_pca_iris

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Incremental principal component analysis (IPCA) is typically used as a replacement for principa...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_incremental_pca_thumb.png
     :alt: Incremental PCA

     :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_incremental_pca

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 a...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_vs_lda_thumb.png
     :alt: Comparison of LDA and PCA 2D projection of Iris dataset

     :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_pca_vs_lda

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example of estimating sources from noisy data.">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_ica_blind_source_separation_thumb.png
     :alt: Blind source separation using FastICA

     :ref:`sphx_glr_auto_examples_decomposition_plot_ica_blind_source_separation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_ica_blind_source_separation

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="These figures aid in illustrating how a point cloud can be very flat in one direction--which is...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_3d_thumb.png
     :alt: Principal components analysis (PCA)

     :ref:`sphx_glr_auto_examples_decomposition_plot_pca_3d.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_pca_3d

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates visually in the feature space a comparison by results using two differ...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_ica_vs_pca_thumb.png
     :alt: FastICA on 2D point clouds

     :ref:`sphx_glr_auto_examples_decomposition_plot_ica_vs_pca.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_ica_vs_pca

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows that Kernel PCA is able to find a projection of the data that makes data lin...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_kernel_pca_thumb.png
     :alt: Kernel PCA

     :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_kernel_pca

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png
     :alt: Model selection with Probabilistic PCA and Factor Analysis (FA)

     :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_pca_vs_fa_model_selection

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Transform a signal as a sparse combination of Ricker wavelets. This example visually compares d...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_sparse_coding_thumb.png
     :alt: Sparse coding with a precomputed dictionary

     :ref:`sphx_glr_auto_examples_decomposition_plot_sparse_coding.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_sparse_coding

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example comparing the effect of reconstructing noisy fragments of a raccoon face image using...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_image_denoising_thumb.png
     :alt: Image denoising using dictionary learning

     :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_image_denoising

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dim...">

.. only:: html

 .. figure:: /auto_examples/decomposition/images/thumb/sphx_glr_plot_faces_decomposition_thumb.png
     :alt: Faces dataset decompositions

     :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/decomposition/plot_faces_decomposition
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_ensemble:

.. _ensemble_examples:

Ensemble methods
----------------

Examples concerning the :mod:`sklearn.ensemble` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of forests of trees to evaluate the impurity-based importance of the...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_forest_importances_faces_thumb.png
     :alt: Pixel importances with a parallel forest of trees

     :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_forest_importances_faces

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D sinusoidal dataset with...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_regression_thumb.png
     :alt: Decision Tree Regression with AdaBoost

     :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_adaboost_regression

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_regressor_thumb.png
     :alt: Plot individual and voting regression predictions

     :ref:`sphx_glr_auto_examples_ensemble_plot_voting_regressor.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_voting_regressor

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This examples shows the use of forests of trees to evaluate the importance of features on an ar...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_forest_importances_thumb.png
     :alt: Feature importances with forests of trees

     :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_forest_importances

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of monotonic constraints on a gradient boosting estimator.">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_monotonic_constraints_thumb.png
     :alt: Monotonic Constraints

     :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_monotonic_constraints

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example using sklearn.ensemble.IsolationForest for anomaly detection.">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_isolation_forest_thumb.png
     :alt: IsolationForest example

     :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_isolation_forest

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset.">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_decision_regions_thumb.png
     :alt: Plot the decision boundaries of a VotingClassifier

     :ref:`sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_voting_decision_regions

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example to compare multi-output regression with random forest and the multiclass meta-estima...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_random_forest_regression_multioutput_thumb.png
     :alt: Comparing random forests and the multi-output meta estimator

     :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_regression_multioutput.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_random_forest_regression_multioutput

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how quantile regression can be used to create prediction intervals.">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_quantile_thumb.png
     :alt: Prediction Intervals for Gradient Boosting Regression

     :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_gradient_boosting_quantile

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Illustration of the effect of different regularization strategies for Gradient Boosting. The ex...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_regularization_thumb.png
     :alt: Gradient Boosting regularization

     :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_gradient_boosting_regularization

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different ...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_voting_probas_thumb.png
     :alt: Plot class probabilities calculated by the VotingClassifier

     :ref:`sphx_glr_auto_examples_ensemble_plot_voting_probas.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_voting_probas

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of w...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_regression_thumb.png
     :alt: Gradient Boosting regression

     :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_gradient_boosting_regression

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The RandomForestClassifier is trained using *bootstrap aggregation*, where each new tree is fit...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_ensemble_oob_thumb.png
     :alt: OOB Errors for Random Forests

     :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_ensemble_oob

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example fits an AdaBoosted decision stump on a non-linearly separable classification datas...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_twoclass_thumb.png
     :alt: Two-class AdaBoost

     :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_adaboost_twoclass

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representati...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_random_forest_embedding_thumb.png
     :alt: Hashing feature transformation using Totally Random Trees

     :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_embedding.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_random_forest_embedding

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example reproduces Figure 1 of Zhu et al [1]_ and shows how boosting can improve predictio...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_multiclass_thumb.png
     :alt: Multi-class AdaBoosted Decision Trees

     :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_adaboost_multiclass

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example is based on Figure 10.2 from Hastie et al 2009 [1]_ and illustrates the difference...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_adaboost_hastie_10_2_thumb.png
     :alt: Discrete versus Real AdaBoost

     :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_hastie_10_2.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_adaboost_hastie_10_2

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Gradient boosting is an ensembling technique where several weak learners (regression trees) are...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png
     :alt: Early stopping of Gradient Boosting

     :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_gradient_boosting_early_stopping

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on t...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_feature_transformation_thumb.png
     :alt: Feature transformations with ensembles of trees

     :ref:`sphx_glr_auto_examples_ensemble_plot_feature_transformation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_feature_transformation

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Out-of-bag (OOB) estimates can be a useful heuristic to estimate the &quot;optimal&quot; number of boosti...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_gradient_boosting_oob_thumb.png
     :alt: Gradient Boosting Out-of-Bag estimates

     :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_gradient_boosting_oob

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares the bias-variance decomposition of the expected mean squa...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_bias_variance_thumb.png
     :alt: Single estimator versus bagging: bias-variance decomposition

     :ref:`sphx_glr_auto_examples_ensemble_plot_bias_variance.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_bias_variance

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the i...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_forest_iris_thumb.png
     :alt: Plot the decision surfaces of ensembles of trees on the iris dataset

     :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_forest_iris

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Stacking refers to a method to blend estimators. In this strategy, some estimators are individu...">

.. only:: html

 .. figure:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_stack_predictors_thumb.png
     :alt: Combine predictors using stacking

     :ref:`sphx_glr_auto_examples_ensemble_plot_stack_predictors.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/ensemble/plot_stack_predictors
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_applications:

.. _realworld_examples:

Examples based on real world datasets
-------------------------------------

Applications to real world problems with some medium sized datasets or
interactive user interface.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is us...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_outlier_detection_wine_thumb.png
     :alt: Outlier detection on a real data set

     :ref:`sphx_glr_auto_examples_applications_plot_outlier_detection_wine.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_outlier_detection_wine

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows the reconstruction of an image from a set of parallel projections, acquired ...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_tomography_l1_reconstruction_thumb.png
     :alt: Compressive sensing: tomography reconstruction with L1 prior (Lasso)

     :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_tomography_l1_reconstruction

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example of applying sklearn.decomposition.NMF and sklearn.decomposition.LatentDirich...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png
     :alt: Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation

     :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_topics_extraction_with_nmf_lda

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is a preprocessed excerpt of the &quot;Labeled Faces in the Wild&quot;, ...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_face_recognition_thumb.png
     :alt: Faces recognition example using eigenfaces and SVMs

     :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_face_recognition

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performa...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_model_complexity_influence_thumb.png
     :alt: Model Complexity Influence

     :ref:`sphx_glr_auto_examples_applications_plot_model_complexity_influence.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_model_complexity_influence

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example employs several unsupervised learning techniques to extract the stock market struc...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_stock_market_thumb.png
     :alt: Visualizing the stock market structure

     :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_stock_market

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A classical way to assert the relative importance of vertices in a graph is to compute the prin...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_wikipedia_principal_eigenvector_thumb.png
     :alt: Wikipedia principal eigenvector

     :ref:`sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/wikipedia_principal_eigenvector

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Modeling species&#x27; geographic distributions is an important problem in conservation biology. In ...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_species_distribution_modeling_thumb.png
     :alt: Species distribution modeling

     :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_species_distribution_modeling

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create da...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_svm_gui_thumb.png
     :alt: Libsvm GUI

     :ref:`sphx_glr_auto_examples_applications_svm_gui.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/svm_gui

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example showing the prediction latency of various scikit-learn estimators.">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_prediction_latency_thumb.png
     :alt: Prediction Latency

     :ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_prediction_latency

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how scikit-learn can be used for classification using an out-of-core...">

.. only:: html

 .. figure:: /auto_examples/applications/images/thumb/sphx_glr_plot_out_of_core_classification_thumb.png
     :alt: Out-of-core classification of text documents

     :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_out_of_core_classification
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_feature_selection:

.. _feature_selection_examples:

Feature Selection
-----------------------

Examples concerning the :mod:`sklearn.feature_selection` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A recursive feature elimination example showing the relevance of pixels in a digit classificati...">

.. only:: html

 .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_rfe_digits_thumb.png
     :alt: Recursive feature elimination

     :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_digits.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/feature_selection/plot_rfe_digits

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the differences between univariate F-test statistics and mutual inform...">

.. only:: html

 .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_f_test_vs_mi_thumb.png
     :alt: Comparison of F-test and mutual information

     :ref:`sphx_glr_auto_examples_feature_selection_plot_f_test_vs_mi.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/feature_selection/plot_f_test_vs_mi

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Simple usage of Pipeline that runs successively a univariate feature selection with anova and t...">

.. only:: html

 .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_feature_selection_pipeline_thumb.png
     :alt: Pipeline Anova SVM

     :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/feature_selection/plot_feature_selection_pipeline

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A recursive feature elimination example with automatic tuning of the number of features selecte...">

.. only:: html

 .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_rfe_with_cross_validation_thumb.png
     :alt: Recursive feature elimination with cross-validation

     :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/feature_selection/plot_rfe_with_cross_validation

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Use SelectFromModel meta-transformer along with Lasso to select the best couple of features fro...">

.. only:: html

 .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_select_from_model_diabetes_thumb.png
     :alt: Feature selection using SelectFromModel and LassoCV

     :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/feature_selection/plot_select_from_model_diabetes

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In order to test if a classification score is significative a technique in repeating the classi...">

.. only:: html

 .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_permutation_test_for_classification_thumb.png
     :alt: Test with permutations the significance of a classification score

     :ref:`sphx_glr_auto_examples_feature_selection_plot_permutation_test_for_classification.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/feature_selection/plot_permutation_test_for_classification

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example showing univariate feature selection.">

.. only:: html

 .. figure:: /auto_examples/feature_selection/images/thumb/sphx_glr_plot_feature_selection_thumb.png
     :alt: Univariate Feature Selection

     :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/feature_selection/plot_feature_selection
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_mixture:

.. _mixture_examples:

Gaussian Mixture Models
-----------------------

Examples concerning the :mod:`sklearn.mixture` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians...">

.. only:: html

 .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_pdf_thumb.png
     :alt: Density Estimation for a Gaussian mixture

     :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/mixture/plot_gmm_pdf

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisa...">

.. only:: html

 .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_thumb.png
     :alt: Gaussian Mixture Model Ellipsoids

     :ref:`sphx_glr_auto_examples_mixture_plot_gmm.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/mixture/plot_gmm

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows that model selection can be performed with Gaussian Mixture Models using inf...">

.. only:: html

 .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_selection_thumb.png
     :alt: Gaussian Mixture Model Selection

     :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/mixture/plot_gmm_selection

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Demonstration of several covariances types for Gaussian mixture models.">

.. only:: html

 .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_covariances_thumb.png
     :alt: GMM covariances

     :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/mixture/plot_gmm_covariances

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the behavior of Gaussian mixture models fit on data that was not samp...">

.. only:: html

 .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_gmm_sin_thumb.png
     :alt: Gaussian Mixture Model Sine Curve

     :ref:`sphx_glr_auto_examples_mixture_plot_gmm_sin.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/mixture/plot_gmm_sin

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitt...">

.. only:: html

 .. figure:: /auto_examples/mixture/images/thumb/sphx_glr_plot_concentration_prior_thumb.png
     :alt: Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture

     :ref:`sphx_glr_auto_examples_mixture_plot_concentration_prior.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/mixture/plot_concentration_prior
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_gaussian_process:

.. _gaussian_process_examples:

Gaussian Process for Machine Learning
-------------------------------------

Examples concerning the :mod:`sklearn.gaussian_process` module.




.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_xor_thumb.png
     :alt: Illustration of Gaussian process classification (GPC) on the XOR dataset

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_xor.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpc_xor

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF ...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_iris_thumb.png
     :alt: Gaussian process classification (GPC) on iris dataset

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_iris.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpc_iris

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Both kernel ridge regression (KRR) and Gaussian process regression (GPR) learn a target functio...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_compare_gpr_krr_thumb.png
     :alt: Comparison of kernel ridge and Gaussian process regression

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_compare_gpr_krr

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the prior and posterior of a GPR with different kernels. Mean, standar...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_prior_posterior_thumb.png
     :alt: Illustration of prior and posterior Gaussian process for different kernels

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_prior_posterior.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpr_prior_posterior

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A two-dimensional classification example showing iso-probability lines for the predicted probab...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_isoprobability_thumb.png
     :alt: Iso-probability lines for Gaussian Processes classification (GPC)

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc_isoprobability.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpc_isoprobability

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the predicted probability of GPC for an RBF kernel with different choi...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpc_thumb.png
     :alt: Probabilistic predictions with Gaussian process classification (GPC)

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpc.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpc

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates that GPR with a sum-kernel including a WhiteKernel can estimate the no...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_noisy_thumb.png
     :alt: Gaussian process regression (GPR) with noise-level estimation

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpr_noisy

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A simple one-dimensional regression example computed in two different ways:">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_noisy_targets_thumb.png
     :alt: Gaussian Processes regression: basic introductory example

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy_targets.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpr_noisy_targets

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example is based on Section 5.4.3 of &quot;Gaussian Processes for Machine Learning&quot; [RW2006]. I...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_co2_thumb.png
     :alt: Gaussian process regression (GPR) on Mauna Loa CO2 data.

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpr_co2

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of Gaussian processes for regression and classification tasks ...">

.. only:: html

 .. figure:: /auto_examples/gaussian_process/images/thumb/sphx_glr_plot_gpr_on_structured_data_thumb.png
     :alt: Gaussian processes on discrete data structures

     :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_on_structured_data.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/gaussian_process/plot_gpr_on_structured_data
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_linear_model:

.. _linear_examples:

Generalized Linear Models
-------------------------

Examples concerning the :mod:`sklearn.linear_model` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_lars_thumb.png
     :alt: Lasso path using LARS

     :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_lasso_lars

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Shows the effect of collinearity in the coefficients of an estimator.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ridge_path_thumb.png
     :alt: Plot Ridge coefficients as a function of the regularization

     :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_path.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_ridge_path

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the maximum margin separating hyperplane within a two-class separable dataset using a line...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_separating_hyperplane_thumb.png
     :alt: SGD: Maximum margin separating hyperplane

     :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sgd_separating_hyperplane

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A plot that compares the various convex loss functions supported by sklearn.linear_model.SGDCla...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_loss_functions_thumb.png
     :alt: SGD: convex loss functions

     :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_loss_functions.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sgd_loss_functions

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Ridge regression is basically minimizing a penalised version of the least-squared function. The...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ols_ridge_variance_thumb.png
     :alt: Ordinary Least Squares and Ridge Regression Variance

     :ref:`sphx_glr_auto_examples_linear_model_plot_ols_ridge_variance.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_ols_ridge_variance

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Ridge Regression is the estimator used in this example. Each color in the left plot represents ...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ridge_coeffs_thumb.png
     :alt: Plot Ridge coefficients as a function of the L2 regularization

     :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_ridge_coeffs

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_penalties_thumb.png
     :alt: SGD: Penalties

     :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_penalties.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sgd_penalties

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Shown in the plot is how the logistic regression would, in this synthetic dataset, classify val...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_thumb.png
     :alt: Logistic function

     :ref:`sphx_glr_auto_examples_linear_model_plot_logistic.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_logistic

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to approximate a function with a polynomial of degree n_degree by...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_polynomial_interpolation_thumb.png
     :alt: Polynomial interpolation

     :ref:`sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_polynomial_interpolation

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" Train l1-penalized logistic regression models on a binary classification problem derived from ...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_path_thumb.png
     :alt: Regularization path of L1- Logistic Regression

     :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_logistic_path

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Show below is a logistic-regression classifiers decision boundaries on the first two dimensions...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_iris_logistic_thumb.png
     :alt: Logistic Regression 3-class Classifier

     :ref:`sphx_glr_auto_examples_linear_model_plot_iris_logistic.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_iris_logistic

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot decision function of a weighted dataset, where the size of points is proportional to its w...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_weighted_samples_thumb.png
     :alt: SGD: Weighted samples

     :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sgd_weighted_samples

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The coefficients, the residual sum of squares and the coefficient of determination are also cal...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ols_thumb.png
     :alt: Linear Regression Example

     :ref:`sphx_glr_auto_examples_linear_model_plot_ols.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_ols

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example we see how to robustly fit a linear model to faulty data using the RANSAC algor...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ransac_thumb.png
     :alt: Robust linear model estimation using RANSAC

     :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_ransac

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that alth...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ols_3d_thumb.png
     :alt: Sparsity Example: Fitting only features 1  and 2

     :ref:`sphx_glr_auto_examples_linear_model_plot_ols_3d.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_ols_3d

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Fit Ridge and HuberRegressor on a dataset with outliers.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_huber_vs_ridge_thumb.png
     :alt: HuberRegressor vs Ridge on dataset with strong outliers

     :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_huber_vs_ridge

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="We show that linear_model.Lasso provides the same results for dense and sparse data and that in...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png
     :alt: Lasso on dense and sparse data

     :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_lasso_dense_vs_sparse_data

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example showing how different online solvers perform on the hand-written digits dataset.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_comparison_thumb.png
     :alt: Comparing various online solvers

     :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_comparison.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sgd_comparison

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected ...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_multi_task_lasso_support_thumb.png
     :alt: Joint feature selection with multi-task Lasso

     :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_multi_task_lasso_support

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits c...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png
     :alt: MNIST classification using multinomial logistic + L1

     :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sparse_logistic_regression_mnist

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the ...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_iris_thumb.png
     :alt: Plot multi-class SGD on the iris dataset

     :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sgd_iris

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encod...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_omp_thumb.png
     :alt: Orthogonal Matching Pursuit

     :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_omp

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Estimates Lasso and Elastic-Net regression models on a manually generated sparse signal corrupt...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_and_elasticnet_thumb.png
     :alt: Lasso and Elastic Net for Sparse Signals

     :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_lasso_and_elasticnet

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Computes a Bayesian Ridge Regression of Sinusoids.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png
     :alt: Curve Fitting with Bayesian Ridge Regression

     :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_bayesian_ridge_curvefit

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Computes a Theil-Sen Regression on a synthetic dataset.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_theilsen_thumb.png
     :alt: Theil-Sen Regression

     :ref:`sphx_glr_auto_examples_linear_model_plot_theilsen.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_theilsen

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corre...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_multinomial_thumb.png
     :alt: Plot multinomial and One-vs-Rest Logistic Regression

     :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_logistic_multinomial

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Here a sine function is fit with a polynomial of order 3, for values close to zero.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_robust_fit_thumb.png
     :alt: Robust linear estimator fitting

     :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_robust_fit

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elast...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png
     :alt: L1 Penalty and Sparsity in Logistic Regression

     :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_logistic_l1_l2_sparsity

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png
     :alt: Lasso and Elastic Net

     :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_lasso_coordinate_descent_path

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Fit regression model with Bayesian Ridge Regression.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_ard_thumb.png
     :alt: Automatic Relevance Determination Regression (ARD)

     :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_ard

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Computes a Bayesian Ridge Regression on a synthetic dataset.">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_bayesian_ridge_thumb.png
     :alt: Bayesian Ridge Regression

     :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_bayesian_ridge

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Use the Akaike information criterion (AIC), the Bayes Information criterion (BIC) and cross-val...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_lasso_model_selection_thumb.png
     :alt: Lasso model selection: Cross-Validation / AIC / BIC

     :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_lasso_model_selection

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression to classify doc...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png
     :alt: Multiclass sparse logistic regression on 20newgroups

     :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a s...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_sgd_early_stopping_thumb.png
     :alt: Early stopping of Stochastic Gradient Descent

     :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_early_stopping.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_sgd_early_stopping

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of log-linear Poisson regression on the `French Motor Third-Pa...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_poisson_regression_non_normal_loss_thumb.png
     :alt: Poisson regression and non-normal loss

     :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_poisson_regression_non_normal_loss

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of Poisson, Gamma and Tweedie regression on the `French Motor ...">

.. only:: html

 .. figure:: /auto_examples/linear_model/images/thumb/sphx_glr_plot_tweedie_regression_insurance_claims_thumb.png
     :alt: Tweedie regression on insurance claims

     :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/linear_model/plot_tweedie_regression_insurance_claims
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_inspection:

.. _inspection_examples:

Inspection
----------

Examples related to the :mod:`sklearn.inspection` module.




.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we compute the permutation importance on the Wisconsin breast cancer dataset u...">

.. only:: html

 .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_permutation_importance_multicollinear_thumb.png
     :alt: Permutation Importance with Multicollinear or Correlated Features

     :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/inspection/plot_permutation_importance_multicollinear

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the impurity-based feature importance of RandomForestClassifie...">

.. only:: html

 .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_permutation_importance_thumb.png
     :alt: Permutation Importance vs Random Forest Feature Importance (MDI)

     :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/inspection/plot_permutation_importance

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Partial dependence plots show the dependence between the target function [2]_ and a set of &#x27;tar...">

.. only:: html

 .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_partial_dependence_thumb.png
     :alt: Partial Dependence Plots

     :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/inspection/plot_partial_dependence

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In linear models, the target value is modeled as a linear combination of the features (see the ...">

.. only:: html

 .. figure:: /auto_examples/inspection/images/thumb/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png
     :alt: Common pitfalls in interpretation of coefficients of linear models

     :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/inspection/plot_linear_model_coefficient_interpretation
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_manifold:

.. _manifold_examples:

Manifold learning
-----------------------

Examples concerning the :mod:`sklearn.manifold` module.




.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An illustration of Swiss Roll reduction with locally linear embedding">

.. only:: html

 .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_swissroll_thumb.png
     :alt: Swiss Roll reduction with LLE

     :ref:`sphx_glr_auto_examples_manifold_plot_swissroll.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/manifold/plot_swissroll

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An illustration of dimensionality reduction on the S-curve dataset with various manifold learni...">

.. only:: html

 .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_compare_methods_thumb.png
     :alt: Comparison of Manifold Learning methods

     :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/manifold/plot_compare_methods

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An illustration of the metric and non-metric MDS on generated noisy data.">

.. only:: html

 .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_mds_thumb.png
     :alt: Multi-dimensional scaling

     :ref:`sphx_glr_auto_examples_manifold_plot_mds.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/manifold/plot_mds

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An illustration of t-SNE on the two concentric circles and the S-curve datasets for different p...">

.. only:: html

 .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_t_sne_perplexity_thumb.png
     :alt: t-SNE: The effect of various perplexity values on the shape

     :ref:`sphx_glr_auto_examples_manifold_plot_t_sne_perplexity.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/manifold/plot_t_sne_perplexity

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An application of the different manifold techniques on a spherical data-set. Here one can see t...">

.. only:: html

 .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_manifold_sphere_thumb.png
     :alt: Manifold Learning methods on a severed sphere

     :ref:`sphx_glr_auto_examples_manifold_plot_manifold_sphere.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/manifold/plot_manifold_sphere

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An illustration of various embeddings on the digits dataset.">

.. only:: html

 .. figure:: /auto_examples/manifold/images/thumb/sphx_glr_plot_lle_digits_thumb.png
     :alt: Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...

     :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/manifold/plot_lle_digits
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_miscellaneous:

.. _miscellaneous_examples:

Miscellaneous
-------------

Miscellaneous and introductory examples for scikit-learn.




.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of the print_changed_only global parameter.">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_changed_only_pprint_parameter_thumb.png
     :alt: Compact estimator representations

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_changed_only_pprint_parameter.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_changed_only_pprint_parameter

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_roc_curve_visualization_api_thumb.png
     :alt: ROC Curve with Visualization API

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_roc_curve_visualization_api

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An illustration of the isotonic regression on generated data. The isotonic regression finds a n...">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_isotonic_regression_thumb.png
     :alt: Isotonic Regression

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_isotonic_regression

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we will construct display objects, ConfusionMatrixDisplay, RocCurveDisplay, an...">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_display_object_visualization_thumb.png
     :alt: Visualizations with Display Objects

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_display_object_visualization

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="    See also sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_partial_dependence_visualization_api_thumb.png
     :alt: Advanced Plotting With Partial Dependence

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_partial_dependence_visualization_api

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of multi-output estimator to complete images. The goal is to predict...">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_multioutput_face_completion_thumb.png
     :alt: Face completion with a multi-output estimators

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_multioutput_face_completion

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example simulates a multi-label document classification problem. The dataset is generated ...">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_multilabel_thumb.png
     :alt: Multilabel classification

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_multilabel

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Da...">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_anomaly_comparison_thumb.png
     :alt: Comparing anomaly detection algorithms for outlier detection on toy datasets

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_anomaly_comparison

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly pr...">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png
     :alt: The Johnson-Lindenstrauss bound for embedding with random projections

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel ...">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_kernel_ridge_regression_thumb.png
     :alt: Comparison of kernel ridge regression and SVR

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_kernel_ridge_regression

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example illustrating the approximation of the feature map of an RBF kernel.">

.. only:: html

 .. figure:: /auto_examples/miscellaneous/images/thumb/sphx_glr_plot_kernel_approximation_thumb.png
     :alt: Explicit feature map approximation for RBF kernels

     :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/miscellaneous/plot_kernel_approximation
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_impute:

.. _impute_examples:

Missing Value Imputation
------------------------

Examples concerning the :mod:`sklearn.impute` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The sklearn.impute.IterativeImputer class is very flexible - it can be used with a variety of e...">

.. only:: html

 .. figure:: /auto_examples/impute/images/thumb/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png
     :alt: Imputing missing values with variants of IterativeImputer

     :ref:`sphx_glr_auto_examples_impute_plot_iterative_imputer_variants_comparison.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/impute/plot_iterative_imputer_variants_comparison

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Missing values can be replaced by the mean, the median or the most frequent value using the bas...">

.. only:: html

 .. figure:: /auto_examples/impute/images/thumb/sphx_glr_plot_missing_values_thumb.png
     :alt: Imputing missing values before building an estimator

     :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/impute/plot_missing_values
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_model_selection:

.. _model_selection_examples:

Model Selection
-----------------------

Examples related to the :mod:`sklearn.model_selection` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use cross_val_predict to visualize prediction errors.">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_cv_predict_thumb.png
     :alt: Plotting Cross-Validated Predictions

     :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_cv_predict

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Example of confusion matrix usage to evaluate the quality of the output of a classifier on the ...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_confusion_matrix_thumb.png
     :alt: Confusion matrix

     :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_confusion_matrix

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this plot you can see the training scores and validation scores of an SVM for different valu...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_validation_curve_thumb.png
     :alt: Plotting Validation Curves

     :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_validation_curve

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the problems of underfitting and overfitting and how we can use linea...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_underfitting_overfitting_thumb.png
     :alt: Underfitting vs. Overfitting

     :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_underfitting_overfitting

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the ...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_grid_search_digits_thumb.png
     :alt: Parameter estimation using grid search with cross-validation

     :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_grid_search_digits

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with S...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_randomized_search_thumb.png
     :alt: Comparing randomized search and grid search for hyperparameter estimation

     :ref:`sphx_glr_auto_examples_model_selection_plot_randomized_search.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_randomized_search

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Illustration of how the performance of an estimator on unseen data (test data) is not the same ...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_train_error_vs_test_error_thumb.png
     :alt: Train error vs Test error

     :ref:`sphx_glr_auto_examples_model_selection_plot_train_error_vs_test_error.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_train_error_vs_test_error

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_roc_crossval_thumb.png
     :alt: Receiver Operating Characteristic (ROC) with cross validation

     :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_roc_crossval

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example compares non-nested and nested cross-validation strategies on a classifier of the ...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_nested_cross_validation_iris_thumb.png
     :alt: Nested versus non-nested cross-validation

     :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_nested_cross_validation_iris

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Multiple metric parameter search can be done by setting the scoring parameter to a list of metr...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_multi_metric_evaluation_thumb.png
     :alt: Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

     :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_multi_metric_evaluation

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is the 20 newsgroups dataset which will be automatically downl...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_grid_search_text_feature_extraction_thumb.png
     :alt: Sample pipeline for text feature extraction and evaluation

     :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/grid_search_text_feature_extraction

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example balances model complexity and cross-validated score by finding a decent accuracy w...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_grid_search_refit_callable_thumb.png
     :alt: Balance model complexity and cross-validated score

     :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_grid_search_refit_callable

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Choosing the right cross-validation object is a crucial part of fitting a model properly. There...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_cv_indices_thumb.png
     :alt: Visualizing cross-validation behavior in scikit-learn

     :ref:`sphx_glr_auto_examples_model_selection_plot_cv_indices.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_cv_indices

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_roc_thumb.png
     :alt: Receiver Operating Characteristic (ROC)

     :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_roc

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Example of Precision-Recall metric to evaluate classifier output quality.">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_precision_recall_thumb.png
     :alt: Precision-Recall

     :ref:`sphx_glr_auto_examples_model_selection_plot_precision_recall.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_precision_recall

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plotting Learning Curves">

.. only:: html

 .. figure:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_learning_curve_thumb.png
     :alt: Plotting Learning Curves

     :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/plot_learning_curve
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_multioutput:

.. _multioutput_examples:

Multioutput methods
-------------------

Examples concerning the :mod:`sklearn.multioutput` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="For this example we will use the `yeast &lt;https://www.openml.org/d/40597&gt;`_ dataset which contai...">

.. only:: html

 .. figure:: /auto_examples/multioutput/images/thumb/sphx_glr_plot_classifier_chain_yeast_thumb.png
     :alt: Classifier Chain

     :ref:`sphx_glr_auto_examples_multioutput_plot_classifier_chain_yeast.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/multioutput/plot_classifier_chain_yeast
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_neighbors:

.. _neighbors_examples:

Nearest Neighbors
-----------------------

Examples concerning the :mod:`sklearn.neighbors` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpola...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_regression_thumb.png
     :alt: Nearest Neighbors regression

     :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_regression

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which comp...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_lof_outlier_detection_thumb.png
     :alt: Outlier detection with Local Outlier Factor (LOF)

     :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_lof_outlier_detection

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_classification_thumb.png
     :alt: Nearest Neighbors Classification

     :ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_classification

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each ...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nearest_centroid_thumb.png
     :alt: Nearest Centroid Classification

     :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_nearest_centroid

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how kernel density estimation (KDE), a powerful non-parametric density estim...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_digits_kde_sampling_thumb.png
     :alt: Kernel Density Estimation

     :ref:`sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_digits_kde_sampling

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This examples demonstrates how to precompute the k nearest neighbors before using them in KNeig...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_caching_nearest_neighbors_thumb.png
     :alt: Caching nearest neighbors

     :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_caching_nearest_neighbors

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates a learned distance metric that maximizes the nearest neighbors classif...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nca_illustration_thumb.png
     :alt: Neighborhood Components Analysis Illustration

     :ref:`sphx_glr_auto_examples_neighbors_plot_nca_illustration.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_nca_illustration

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which comp...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_lof_novelty_detection_thumb.png
     :alt: Novelty detection with Local Outlier Factor (LOF)

     :ref:`sphx_glr_auto_examples_neighbors_plot_lof_novelty_detection.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_lof_novelty_detection

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example comparing nearest neighbors classification with and without Neighborhood Components ...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nca_classification_thumb.png
     :alt: Comparing Nearest Neighbors with and without Neighborhood Components Analysis

     :ref:`sphx_glr_auto_examples_neighbors_plot_nca_classification.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_nca_classification

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Neighborhood Components Analysis for dimensionality reduction.">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_nca_dim_reduction_thumb.png
     :alt: Dimensionality Reduction with Neighborhood Components Analysis

     :ref:`sphx_glr_auto_examples_neighbors_plot_nca_dim_reduction.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_nca_dim_reduction

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example does not perform any learning over the data (see sphx_glr_auto_examples_applicatio...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_species_kde_thumb.png
     :alt: Kernel Density Estimate of Species Distributions

     :ref:`sphx_glr_auto_examples_neighbors_plot_species_kde.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_species_kde

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The first plot shows one of the problems with using histograms to visualize the density of poin...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_plot_kde_1d_thumb.png
     :alt: Simple 1D Kernel Density Estimation

     :ref:`sphx_glr_auto_examples_neighbors_plot_kde_1d.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/plot_kde_1d

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example presents how to chain KNeighborsTransformer and TSNE in a pipeline. It also shows ...">

.. only:: html

 .. figure:: /auto_examples/neighbors/images/thumb/sphx_glr_approximate_nearest_neighbors_thumb.png
     :alt: Approximate nearest neighbors in TSNE

     :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neighbors/approximate_nearest_neighbors
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_neural_networks:

.. _neural_network_examples:

Neural Networks
-----------------------

Examples concerning the :mod:`sklearn.neural_network` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Sometimes looking at the learned coefficients of a neural network can provide insight into the ...">

.. only:: html

 .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_mnist_filters_thumb.png
     :alt: Visualization of MLP weights on MNIST

     :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neural_networks/plot_mnist_filters

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="For greyscale image data where pixel values can be interpreted as degrees of blackness on a whi...">

.. only:: html

 .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_rbm_logistic_classification_thumb.png
     :alt: Restricted Boltzmann Machine features for digit classification

     :ref:`sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neural_networks/plot_rbm_logistic_classification

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example visualizes some training loss curves for different stochastic learning strategies,...">

.. only:: html

 .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_mlp_training_curves_thumb.png
     :alt: Compare Stochastic learning strategies for MLPClassifier

     :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neural_networks/plot_mlp_training_curves

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A comparison of different values for regularization parameter &#x27;alpha&#x27; on synthetic datasets. Th...">

.. only:: html

 .. figure:: /auto_examples/neural_networks/images/thumb/sphx_glr_plot_mlp_alpha_thumb.png
     :alt: Varying regularization in Multi-layer Perceptron

     :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/neural_networks/plot_mlp_alpha
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_compose:

.. _compose_examples:

Pipelines and composite estimators
----------------------------------

Examples of how to compose transformers and pipelines from other estimators. See the :ref:`User Guide <combining_estimators>`.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In many real-world examples, there are many ways to extract features from a dataset. Often it i...">

.. only:: html

 .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_feature_union_thumb.png
     :alt: Concatenating multiple feature extraction methods

     :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/compose/plot_feature_union

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The PCA does an unsupervised dimensionality reduction, while the logistic regression does the p...">

.. only:: html

 .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_digits_pipe_thumb.png
     :alt: Pipelining: chaining a PCA and a logistic regression

     :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/compose/plot_digits_pipe

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example constructs a pipeline that does dimensionality reduction followed by prediction wi...">

.. only:: html

 .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_compare_reduction_thumb.png
     :alt: Selecting dimensionality reduction with Pipeline and GridSearchCV

     :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/compose/plot_compare_reduction

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how to apply different preprocessing and feature extraction pipelines ...">

.. only:: html

 .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_column_transformer_mixed_types_thumb.png
     :alt: Column Transformer with Mixed Types

     :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/compose/plot_column_transformer_mixed_types

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Datasets can often contain components that require different feature extraction and processing ...">

.. only:: html

 .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_column_transformer_thumb.png
     :alt: Column Transformer with Heterogeneous Data Sources

     :ref:`sphx_glr_auto_examples_compose_plot_column_transformer.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/compose/plot_column_transformer

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we give an overview of the sklearn.compose.TransformedTargetRegressor. Two exa...">

.. only:: html

 .. figure:: /auto_examples/compose/images/thumb/sphx_glr_plot_transformed_target_thumb.png
     :alt: Effect of transforming the targets in regression model

     :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/compose/plot_transformed_target
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_preprocessing:

.. _preprocessing_examples:

Preprocessing
-------------

Examples concerning the :mod:`sklearn.preprocessing` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Shows how to use a function transformer in a pipeline. If you know your dataset&#x27;s first princip...">

.. only:: html

 .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_function_transformer_thumb.png
     :alt: Using FunctionTransformer to select columns

     :ref:`sphx_glr_auto_examples_preprocessing_plot_function_transformer.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/preprocessing/plot_function_transformer

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The example compares prediction result of linear regression (linear model) and decision tree (t...">

.. only:: html

 .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_discretization_thumb.png
     :alt: Using KBinsDiscretizer to discretize continuous features

     :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/preprocessing/plot_discretization

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example presents the different strategies implemented in KBinsDiscretizer:">

.. only:: html

 .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_discretization_strategies_thumb.png
     :alt: Demonstrating the different strategies of KBinsDiscretizer

     :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/preprocessing/plot_discretization_strategies

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Feature scaling through standardization (or Z-score normalization) can be an important preproce...">

.. only:: html

 .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_scaling_importance_thumb.png
     :alt: Importance of Feature Scaling

     :ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/preprocessing/plot_scaling_importance

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms through PowerTransf...">

.. only:: html

 .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_map_data_to_normal_thumb.png
     :alt: Map data to a normal distribution

     :ref:`sphx_glr_auto_examples_preprocessing_plot_map_data_to_normal.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/preprocessing/plot_map_data_to_normal

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A demonstration of feature discretization on synthetic classification datasets. Feature discret...">

.. only:: html

 .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_discretization_classification_thumb.png
     :alt: Feature discretization

     :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/preprocessing/plot_discretization_classification

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Feature 0 (median income in a block) and feature 5 (number of households) of the `California ho...">

.. only:: html

 .. figure:: /auto_examples/preprocessing/images/thumb/sphx_glr_plot_all_scaling_thumb.png
     :alt: Compare the effect of different scalers on data with outliers

     :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/preprocessing/plot_all_scaling
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_semi_supervised:

.. _semi_supervised_examples:

Semi Supervised Classification
------------------------------

Examples concerning the :mod:`sklearn.semi_supervised` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Comparison for decision boundary generated on iris dataset between Label Propagation and SVM.">

.. only:: html

 .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_versus_svm_iris_thumb.png
     :alt: Decision boundary of label propagation versus SVM on the Iris dataset

     :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_versus_svm_iris.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Example of LabelPropagation learning a complex internal structure to demonstrate &quot;manifold lear...">

.. only:: html

 .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_structure_thumb.png
     :alt: Label Propagation learning a complex structure

     :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/semi_supervised/plot_label_propagation_structure

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading mo...">

.. only:: html

 .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_thumb.png
     :alt: Label Propagation digits: Demonstrating performance

     :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/semi_supervised/plot_label_propagation_digits

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation.">

.. only:: html

 .. figure:: /auto_examples/semi_supervised/images/thumb/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png
     :alt: Label Propagation digits active learning

     :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/semi_supervised/plot_label_propagation_digits_active_learning
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_svm:

.. _svm_examples:

Support Vector Machines
-----------------------

Examples concerning the :mod:`sklearn.svm` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Perform binary classification using non-linear SVC with RBF kernel. The target to predict is a ...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_nonlinear_thumb.png
     :alt: Non-linear SVM

     :ref:`sphx_glr_auto_examples_svm_plot_svm_nonlinear.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_svm_nonlinear

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot the maximum margin separating hyperplane within a two-class separable dataset using a Supp...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_separating_hyperplane_thumb.png
     :alt: SVM: Maximum margin separating hyperplane

     :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_separating_hyperplane

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_custom_kernel_thumb.png
     :alt: SVM with custom kernel

     :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_custom_kernel

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vecto...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_linearsvc_support_vectors_thumb.png
     :alt: Plot the support vectors in LinearSVC

     :ref:`sphx_glr_auto_examples_svm_plot_linearsvc_support_vectors.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_linearsvc_support_vectors

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The two plots differ only in the area in the middle where the classes are tied. If break_ties=F...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_tie_breaking_thumb.png
     :alt: SVM Tie Breaking Example

     :ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_svm_tie_breaking

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot decision function of a weighted dataset, where the size of points is proportional to its w...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_weighted_samples_thumb.png
     :alt: SVM: Weighted samples

     :ref:`sphx_glr_auto_examples_svm_plot_weighted_samples.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_weighted_samples

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Find the optimal separating hyperplane using an SVC for classes that are unbalanced.">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png
     :alt: SVM: Separating hyperplane for unbalanced classes

     :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_separating_hyperplane_unbalanced

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Three different types of SVM-Kernels are displayed below. The polynomial and RBF are especially...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_kernels_thumb.png
     :alt: SVM-Kernels

     :ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_svm_kernels

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform univariate feature selection before running a SVC (support ve...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_anova_thumb.png
     :alt: SVM-Anova: SVM with univariate feature selection

     :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_svm_anova

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Toy example of 1D regression using linear, polynomial and RBF kernels.">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_regression_thumb.png
     :alt: Support Vector Regression (SVR) using linear and non-linear kernels

     :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_svm_regression

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A small value of C includes more/all the observations, allowing the margins to be calculated us...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_margin_thumb.png
     :alt: SVM Margins Example

     :ref:`sphx_glr_auto_examples_svm_plot_svm_margin.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_svm_margin

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="An example using a one-class SVM for novelty detection.">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_oneclass_thumb.png
     :alt: One-class SVM with non-linear kernel (RBF)

     :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_oneclass

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only ...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_iris_svc_thumb.png
     :alt: Plot different SVM classifiers in the iris dataset

     :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_iris_svc

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The following example illustrates the effect of scaling the regularization parameter when using...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_svm_scale_c_thumb.png
     :alt: Scaling the regularization parameter for SVCs

     :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_svm_scale_c

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of the parameters gamma and C of the Radial Basis Function ...">

.. only:: html

 .. figure:: /auto_examples/svm/images/thumb/sphx_glr_plot_rbf_parameters_thumb.png
     :alt: RBF SVM parameters

     :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/svm/plot_rbf_parameters
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_exercises:

Tutorial exercises
------------------

Exercises for the tutorials



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise regarding the use of classification techniques on the Digits dataset.">

.. only:: html

 .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_digits_classification_exercise_thumb.png
     :alt: Digits Classification Exercise

     :ref:`sphx_glr_auto_examples_exercises_plot_digits_classification_exercise.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/exercises/plot_digits_classification_exercise

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise using Cross-validation with an SVM on the Digits dataset.">

.. only:: html

 .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_cv_digits_thumb.png
     :alt: Cross-validation on Digits Dataset Exercise

     :ref:`sphx_glr_auto_examples_exercises_plot_cv_digits.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/exercises/plot_cv_digits

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise for using different SVM kernels.">

.. only:: html

 .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_iris_exercise_thumb.png
     :alt: SVM Exercise

     :ref:`sphx_glr_auto_examples_exercises_plot_iris_exercise.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/exercises/plot_iris_exercise

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise which uses cross-validation with linear models.">

.. only:: html

 .. figure:: /auto_examples/exercises/images/thumb/sphx_glr_plot_cv_diabetes_thumb.png
     :alt: Cross-validation on diabetes Dataset Exercise

     :ref:`sphx_glr_auto_examples_exercises_plot_cv_diabetes.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/exercises/plot_cv_diabetes
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. _sphx_glr_auto_examples_text:

.. _text_examples:

Working with text documents
----------------------------

Examples concerning the :mod:`sklearn.feature_extraction.text` module.



.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Compares FeatureHasher and DictVectorizer by using both to vectorize text documents.">

.. only:: html

 .. figure:: /auto_examples/text/images/thumb/sphx_glr_plot_hashing_vs_dict_vectorizer_thumb.png
     :alt: FeatureHasher and DictVectorizer Comparison

     :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/text/plot_hashing_vs_dict_vectorizer

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how the scikit-learn can be used to cluster documents by topics usin...">

.. only:: html

 .. figure:: /auto_examples/text/images/thumb/sphx_glr_plot_document_clustering_thumb.png
     :alt: Clustering text documents using k-means

     :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/text/plot_document_clustering

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how scikit-learn can be used to classify documents by topics using a...">

.. only:: html

 .. figure:: /auto_examples/text/images/thumb/sphx_glr_plot_document_classification_20newsgroups_thumb.png
     :alt: Classification of text documents using sparse features

     :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/text/plot_document_classification_20newsgroups
.. raw:: html

    <div class="sphx-glr-clear"></div>



.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-gallery


  .. container:: sphx-glr-download sphx-glr-download-python

    :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
