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<div class="section" id="examples">
<span id="general-examples"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1>
<div class="section" id="miscellaneous-examples">
<h2>Miscellaneous examples<a class="headerlink" href="#miscellaneous-examples" title="Permalink to this headline">¶</a></h2>
<p>Miscellaneous and introductory examples for scikit-learn.</p>
<div class="sphx-glr-thumbcontainer" tooltip="The PCA does an unsupervised dimensionality reduction, while the logistic regression does the p..."><div class="figure" id="id2">
<img alt="../_images/sphx_glr_plot_digits_pipe_thumb.png" src="../_images/sphx_glr_plot_digits_pipe_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_digits_pipe.html#sphx-glr-auto-examples-plot-digits-pipe-py"><span class="std std-ref">Pipelining: chaining a PCA and a logistic regression</span></a></span></p>
</div>
</div><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer" tooltip="In many real-world examples, there are many ways to extract features from a dataset. Often it i..."><div class="figure" id="id3">
<img alt="../_images/sphx_glr_plot_feature_stacker_thumb.png" src="../_images/sphx_glr_plot_feature_stacker_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_feature_stacker.html#sphx-glr-auto-examples-plot-feature-stacker-py"><span class="std std-ref">Concatenating multiple feature extraction methods</span></a></span></p>
</div>
</div><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer" tooltip="An illustration of the isotonic regression on generated data. The isotonic regression finds a n..."><div class="figure" id="id4">
<img alt="../_images/sphx_glr_plot_isotonic_regression_thumb.png" src="../_images/sphx_glr_plot_isotonic_regression_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_isotonic_regression.html#sphx-glr-auto-examples-plot-isotonic-regression-py"><span class="std std-ref">Isotonic Regression</span></a></span></p>
</div>
</div><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of multi-output estimator to complete images. The goal is to predict..."><div class="figure" id="id5">
<img alt="../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" src="../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_multioutput_face_completion.html#sphx-glr-auto-examples-plot-multioutput-face-completion-py"><span class="std std-ref">Face completion with a multi-output estimators</span></a></span></p>
</div>
</div><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer" tooltip="This example constructs a pipeline that does dimensionality reduction followed by prediction wi..."><div class="figure" id="id6">
<img alt="../_images/sphx_glr_plot_compare_reduction_thumb.png" src="../_images/sphx_glr_plot_compare_reduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_compare_reduction.html#sphx-glr-auto-examples-plot-compare-reduction-py"><span class="std std-ref">Selecting dimensionality reduction with Pipeline and GridSearchCV</span></a></span></p>
</div>
</div><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer" tooltip="This example simulates a multi-label document classification problem. The dataset is generated ..."><div class="figure" id="id7">
<img alt="../_images/sphx_glr_plot_multilabel_thumb.png" src="../_images/sphx_glr_plot_multilabel_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_multilabel.html#sphx-glr-auto-examples-plot-multilabel-py"><span class="std std-ref">Multilabel classification</span></a></span></p>
</div>
</div><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Da..."><div class="figure" id="id8">
<img alt="../_images/sphx_glr_plot_anomaly_comparison_thumb.png" src="../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></span></p>
</div>
</div><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer" tooltip="Datasets can often contain components of that require different feature extraction and processi..."><div class="figure" id="id9">
<img alt="../_images/sphx_glr_column_transformer_thumb.png" src="../_images/sphx_glr_column_transformer_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="column_transformer.html#sphx-glr-auto-examples-column-transformer-py"><span class="std std-ref">Column Transformer with Heterogeneous Data Sources</span></a></span></p>
</div>
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</div>
<div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly pr..."><div class="figure" id="id10">
<img alt="../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" src="../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></span></p>
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</div>
<div class="sphx-glr-thumbcontainer" tooltip="Missing values can be replaced by the mean, the median or the most frequent value using the bas..."><div class="figure" id="id11">
<img alt="../_images/sphx_glr_plot_missing_values_thumb.png" src="../_images/sphx_glr_plot_missing_values_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_missing_values.html#sphx-glr-auto-examples-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a></span></p>
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</div>
<div class="sphx-glr-thumbcontainer" tooltip="Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel ..."><div class="figure" id="id12">
<img alt="../_images/sphx_glr_plot_kernel_ridge_regression_thumb.png" src="../_images/sphx_glr_plot_kernel_ridge_regression_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_kernel_ridge_regression.html#sphx-glr-auto-examples-plot-kernel-ridge-regression-py"><span class="std std-ref">Comparison of kernel ridge regression and SVR</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="An example illustrating the approximation of the feature map of an RBF kernel."><div class="figure" id="id13">
<img alt="../_images/sphx_glr_plot_kernel_approximation_thumb.png" src="../_images/sphx_glr_plot_kernel_approximation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="plot_kernel_approximation.html#sphx-glr-auto-examples-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="examples-based-on-real-world-datasets">
<span id="realworld-examples"></span><h2>Examples based on real world datasets<a class="headerlink" href="#examples-based-on-real-world-datasets" title="Permalink to this headline">¶</a></h2>
<p>Applications to real world problems with some medium sized datasets or
interactive user interface.</p>
<div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is us..."><div class="figure" id="id14">
<img alt="../_images/sphx_glr_plot_outlier_detection_housing_thumb.png" src="../_images/sphx_glr_plot_outlier_detection_housing_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_outlier_detection_housing.html#sphx-glr-auto-examples-applications-plot-outlier-detection-housing-py"><span class="std std-ref">Outlier detection on a real data set</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example shows the reconstruction of an image from a set of parallel projections, acquired ..."><div class="figure" id="id15">
<img alt="../_images/sphx_glr_plot_tomography_l1_reconstruction_thumb.png" src="../_images/sphx_glr_plot_tomography_l1_reconstruction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py"><span class="std std-ref">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This is an example of applying :class:`sklearn.decomposition.NMF` and :class:`sklearn.decomposi..."><div class="figure" id="id16">
<img alt="../_images/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png" src="../_images/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py"><span class="std std-ref">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", ..."><div class="figure" id="id17">
<img alt="../_images/sphx_glr_plot_face_recognition_thumb.png" src="../_images/sphx_glr_plot_face_recognition_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py"><span class="std std-ref">Faces recognition example using eigenfaces and SVMs</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performa..."><div class="figure" id="id18">
<img alt="../_images/sphx_glr_plot_model_complexity_influence_thumb.png" src="../_images/sphx_glr_plot_model_complexity_influence_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py"><span class="std std-ref">Model Complexity Influence</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Modeling species' geographic distributions is an important problem in conservation biology. In ..."><div class="figure" id="id19">
<img alt="../_images/sphx_glr_plot_species_distribution_modeling_thumb.png" src="../_images/sphx_glr_plot_species_distribution_modeling_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Species distribution modeling</span></a></span></p>
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</div>
<div class="sphx-glr-thumbcontainer" tooltip="This example employs several unsupervised learning techniques to extract the stock market struc..."><div class="figure" id="id20">
<img alt="../_images/sphx_glr_plot_stock_market_thumb.png" src="../_images/sphx_glr_plot_stock_market_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py"><span class="std std-ref">Visualizing the stock market structure</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="A classical way to assert the relative importance of vertices in a graph is to compute the prin..."><div class="figure" id="id21">
<img alt="../_images/sphx_glr_wikipedia_principal_eigenvector_thumb.png" src="../_images/sphx_glr_wikipedia_principal_eigenvector_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/wikipedia_principal_eigenvector.html#sphx-glr-auto-examples-applications-wikipedia-principal-eigenvector-py"><span class="std std-ref">Wikipedia principal eigenvector</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create da..."><div class="figure" id="id22">
<img alt="../_images/sphx_glr_svm_gui_thumb.png" src="../_images/sphx_glr_svm_gui_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/svm_gui.html#sphx-glr-auto-examples-applications-svm-gui-py"><span class="std std-ref">Libsvm GUI</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This is an example showing the prediction latency of various scikit-learn estimators."><div class="figure" id="id23">
<img alt="../_images/sphx_glr_plot_prediction_latency_thumb.png" src="../_images/sphx_glr_plot_prediction_latency_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py"><span class="std std-ref">Prediction Latency</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how scikit-learn can be used for classification using an out-of-core..."><div class="figure" id="id24">
<img alt="../_images/sphx_glr_plot_out_of_core_classification_thumb.png" src="../_images/sphx_glr_plot_out_of_core_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="applications/plot_out_of_core_classification.html#sphx-glr-auto-examples-applications-plot-out-of-core-classification-py"><span class="std std-ref">Out-of-core classification of text documents</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="biclustering">
<span id="bicluster-examples"></span><h2>Biclustering<a class="headerlink" href="#biclustering" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.cluster.bicluster" title="sklearn.cluster.bicluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster.bicluster</span></code></a> module.</p>
<div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clus..."><div class="figure" id="id25">
<img alt="../_images/sphx_glr_plot_spectral_coclustering_thumb.png" src="../_images/sphx_glr_plot_spectral_coclustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="bicluster/plot_spectral_coclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-coclustering-py"><span class="std std-ref">A demo of the Spectral Co-Clustering algorithm</span></a></span></p>
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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..."><div class="figure" id="id26">
<img alt="../_images/sphx_glr_plot_spectral_biclustering_thumb.png" src="../_images/sphx_glr_plot_spectral_biclustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py"><span class="std std-ref">A demo of the Spectral Biclustering algorithm</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset..."><div class="figure" id="id27">
<img alt="../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" src="../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">Biclustering documents with the Spectral Co-clustering algorithm</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="calibration">
<span id="calibration-examples"></span><h2>Calibration<a class="headerlink" href="#calibration" title="Permalink to this headline">¶</a></h2>
<p>Examples illustrating the calibration of predicted probabilities of classifiers.</p>
<div class="sphx-glr-thumbcontainer" tooltip="Well calibrated classifiers are probabilistic classifiers for which the output of the predict_p..."><div class="figure" id="id28">
<img alt="../_images/sphx_glr_plot_compare_calibration_thumb.png" src="../_images/sphx_glr_plot_compare_calibration_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a></span></p>
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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..."><div class="figure" id="id29">
<img alt="../_images/sphx_glr_plot_calibration_curve_thumb.png" src="../_images/sphx_glr_plot_calibration_curve_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py"><span class="std std-ref">Probability Calibration curves</span></a></span></p>
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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..."><div class="figure" id="id30">
<img alt="../_images/sphx_glr_plot_calibration_thumb.png" src="../_images/sphx_glr_plot_calibration_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py"><span class="std std-ref">Probability calibration of classifiers</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class ..."><div class="figure" id="id31">
<img alt="../_images/sphx_glr_plot_calibration_multiclass_thumb.png" src="../_images/sphx_glr_plot_calibration_multiclass_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="calibration/plot_calibration_multiclass.html#sphx-glr-auto-examples-calibration-plot-calibration-multiclass-py"><span class="std std-ref">Probability Calibration for 3-class classification</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="classification">
<span id="classification-examples"></span><h2>Classification<a class="headerlink" href="#classification" title="Permalink to this headline">¶</a></h2>
<p>General examples about classification algorithms.</p>
<div class="sphx-glr-thumbcontainer" tooltip="An example showing how the scikit-learn can be used to recognize images of hand-written digits."><div class="figure" id="id32">
<img alt="../_images/sphx_glr_plot_digits_classification_thumb.png" src="../_images/sphx_glr_plot_digits_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py"><span class="std std-ref">Recognizing hand-written digits</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Shows how shrinkage improves classification. "><div class="figure" id="id33">
<img alt="../_images/sphx_glr_plot_lda_thumb.png" src="../_images/sphx_glr_plot_lda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py"><span class="std std-ref">Normal and Shrinkage Linear Discriminant Analysis for classification</span></a></span></p>
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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..."><div class="figure" id="id34">
<img alt="../_images/sphx_glr_plot_classification_probability_thumb.png" src="../_images/sphx_glr_plot_classification_probability_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a></span></p>
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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 ..."><div class="figure" id="id35">
<img alt="../_images/sphx_glr_plot_classifier_comparison_thumb.png" src="../_images/sphx_glr_plot_classifier_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a></span></p>
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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..."><div class="figure" id="id36">
<img alt="../_images/sphx_glr_plot_lda_qda_thumb.png" src="../_images/sphx_glr_plot_lda_qda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py"><span class="std std-ref">Linear and Quadratic Discriminant Analysis with covariance ellipsoid</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="clustering">
<span id="cluster-examples"></span><h2>Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></a> module.</p>
<div class="sphx-glr-thumbcontainer" tooltip="These images how similar features are merged together using feature agglomeration. "><div class="figure" id="id37">
<img alt="../_images/sphx_glr_plot_digits_agglomeration_thumb.png" src="../_images/sphx_glr_plot_digits_agglomeration_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py"><span class="std std-ref">Feature agglomeration</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Reference:"><div class="figure" id="id38">
<img alt="../_images/sphx_glr_plot_mean_shift_thumb.png" src="../_images/sphx_glr_plot_mean_shift_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py"><span class="std std-ref">A demo of the mean-shift clustering algorithm</span></a></span></p>
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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..."><div class="figure" id="id39">
<img alt="../_images/sphx_glr_plot_kmeans_assumptions_thumb.png" src="../_images/sphx_glr_plot_kmeans_assumptions_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py"><span class="std std-ref">Demonstration of k-means assumptions</span></a></span></p>
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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..."><div class="figure" id="id40">
<img alt="../_images/sphx_glr_plot_dict_face_patches_thumb.png" src="../_images/sphx_glr_plot_dict_face_patches_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py"><span class="std std-ref">Online learning of a dictionary of parts of faces</span></a></span></p>
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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 us..."><div class="figure" id="id41">
<img alt="../_images/sphx_glr_plot_face_compress_thumb.png" src="../_images/sphx_glr_plot_face_compress_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_face_compress.html#sphx-glr-auto-examples-cluster-plot-face-compress-py"><span class="std std-ref">Vector Quantization Example</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example uses :ref:`spectral_clustering` on a graph created from voxel-to-voxel difference ..."><div class="figure" id="id42">
<img alt="../_images/sphx_glr_plot_coin_segmentation_thumb.png" src="../_images/sphx_glr_plot_coin_segmentation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py"><span class="std std-ref">Segmenting the picture of greek coins in regions</span></a></span></p>
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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..."><div class="figure" id="id43">
<img alt="../_images/sphx_glr_plot_coin_ward_segmentation_thumb.png" src="../_images/sphx_glr_plot_coin_ward_segmentation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_coin_ward_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-ward-segmentation-py"><span class="std std-ref">A demo of structured Ward hierarchical clustering on an image of coins</span></a></span></p>
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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..."><div class="figure" id="id44">
<img alt="../_images/sphx_glr_plot_agglomerative_clustering_thumb.png" src="../_images/sphx_glr_plot_agglomerative_clustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py"><span class="std std-ref">Agglomerative clustering with and without structure</span></a></span></p>
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</div>
<div class="sphx-glr-thumbcontainer" tooltip="Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Poin..."><div class="figure" id="id45">
<img alt="../_images/sphx_glr_plot_affinity_propagation_thumb.png" src="../_images/sphx_glr_plot_affinity_propagation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_affinity_propagation.html#sphx-glr-auto-examples-cluster-plot-affinity-propagation-py"><span class="std std-ref">Demo of affinity propagation clustering algorithm</span></a></span></p>
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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..."><div class="figure" id="id46">
<img alt="../_images/sphx_glr_plot_cluster_iris_thumb.png" src="../_images/sphx_glr_plot_cluster_iris_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_cluster_iris.html#sphx-glr-auto-examples-cluster-plot-cluster-iris-py"><span class="std std-ref">K-means Clustering</span></a></span></p>
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</div>
<div class="sphx-glr-thumbcontainer" tooltip="An illustration of various linkage option for agglomerative clustering on a 2D embedding of the..."><div class="figure" id="id47">
<img alt="../_images/sphx_glr_plot_digits_linkage_thumb.png" src="../_images/sphx_glr_plot_digits_linkage_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py"><span class="std std-ref">Various Agglomerative Clustering on a 2D embedding of digits</span></a></span></p>
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</div>
<div class="sphx-glr-thumbcontainer" tooltip="In this example, an image with connected circles is generated and spectral clustering is used t..."><div class="figure" id="id48">
<img alt="../_images/sphx_glr_plot_segmentation_toy_thumb.png" src="../_images/sphx_glr_plot_segmentation_toy_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_segmentation_toy.html#sphx-glr-auto-examples-cluster-plot-segmentation-toy-py"><span class="std std-ref">Spectral clustering for image segmentation</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Finds core samples of high density and expands clusters from them."><div class="figure" id="id49">
<img alt="../_images/sphx_glr_plot_dbscan_thumb.png" src="../_images/sphx_glr_plot_dbscan_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py"><span class="std std-ref">Demo of DBSCAN clustering algorithm</span></a></span></p>
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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..."><div class="figure" id="id50">
<img alt="../_images/sphx_glr_plot_color_quantization_thumb.png" src="../_images/sphx_glr_plot_color_quantization_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_color_quantization.html#sphx-glr-auto-examples-cluster-plot-color-quantization-py"><span class="std std-ref">Color Quantization using K-Means</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Example builds a swiss roll dataset and runs hierarchical clustering on their position."><div class="figure" id="id51">
<img alt="../_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png" src="../_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_ward_structured_vs_unstructured.html#sphx-glr-auto-examples-cluster-plot-ward-structured-vs-unstructured-py"><span class="std std-ref">Hierarchical clustering: structured vs unstructured ward</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the effect of different metrics on the hierarchical clustering."><div class="figure" id="id52">
<img alt="../_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png" src="../_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py"><span class="std std-ref">Agglomerative clustering with different metrics</span></a></span></p>
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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..."><div class="figure" id="id53">
<img alt="../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" src="../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py"><span class="std std-ref">Compare BIRCH and MiniBatchKMeans</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Evaluate the ability of k-means initializations strategies to make the algorithm convergence ro..."><div class="figure" id="id54">
<img alt="../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png" src="../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py"><span class="std std-ref">Empirical evaluation of the impact of k-means initialization</span></a></span></p>
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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..."><div class="figure" id="id55">
<img alt="../_images/sphx_glr_plot_adjusted_for_chance_measures_thumb.png" src="../_images/sphx_glr_plot_adjusted_for_chance_measures_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py"><span class="std std-ref">Adjustment for chance in clustering performance evaluation</span></a></span></p>
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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..."><div class="figure" id="id56">
<img alt="../_images/sphx_glr_plot_kmeans_digits_thumb.png" src="../_images/sphx_glr_plot_kmeans_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py"><span class="std std-ref">A demo of K-Means clustering on the handwritten digits data</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example compares 2 dimensionality reduction strategies:"><div class="figure" id="id57">
<img alt="../_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png" src="../_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py"><span class="std std-ref">Feature agglomeration vs. univariate selection</span></a></span></p>
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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..."><div class="figure" id="id58">
<img alt="../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" src="../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py"><span class="std std-ref">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different linkage methods for hierarchical clustering on ..."><div class="figure" id="id59">
<img alt="../_images/sphx_glr_plot_linkage_comparison_thumb.png" src="../_images/sphx_glr_plot_linkage_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py"><span class="std std-ref">Comparing different hierarchical linkage methods on toy datasets</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Silhouette analysis can be used to study the separation distance between the resulting clusters..."><div class="figure" id="id60">
<img alt="../_images/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png" src="../_images/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py"><span class="std std-ref">Selecting the number of clusters with silhouette analysis on KMeans clustering</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are "int..."><div class="figure" id="id61">
<img alt="../_images/sphx_glr_plot_cluster_comparison_thumb.png" src="../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="covariance-estimation">
<span id="covariance-examples"></span><h2>Covariance estimation<a class="headerlink" href="#covariance-estimation" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.covariance</span></code></a> module.</p>
<div class="sphx-glr-thumbcontainer" tooltip="The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and..."><div class="figure" id="id62">
<img alt="../_images/sphx_glr_plot_lw_vs_oas_thumb.png" src="../_images/sphx_glr_plot_lw_vs_oas_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py"><span class="std std-ref">Ledoit-Wolf vs OAS estimation</span></a></span></p>
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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..."><div class="figure" id="id63">
<img alt="../_images/sphx_glr_plot_sparse_cov_thumb.png" src="../_images/sphx_glr_plot_sparse_cov_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py"><span class="std std-ref">Sparse inverse covariance estimation</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="When working with covariance estimation, the usual approach is to use a maximum likelihood esti..."><div class="figure" id="id64">
<img alt="../_images/sphx_glr_plot_covariance_estimation_thumb.png" src="../_images/sphx_glr_plot_covariance_estimation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py"><span class="std std-ref">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="An example to show covariance estimation with the Mahalanobis distances on Gaussian distributed..."><div class="figure" id="id65">
<img alt="../_images/sphx_glr_plot_mahalanobis_distances_thumb.png" src="../_images/sphx_glr_plot_mahalanobis_distances_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="covariance/plot_mahalanobis_distances.html#sphx-glr-auto-examples-covariance-plot-mahalanobis-distances-py"><span class="std std-ref">Robust covariance estimation and Mahalanobis distances relevance</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="When the amount of contamination is known, this example illustrates three different ways of per..."><div class="figure" id="id66">
<img alt="../_images/sphx_glr_plot_outlier_detection_thumb.png" src="../_images/sphx_glr_plot_outlier_detection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="covariance/plot_outlier_detection.html#sphx-glr-auto-examples-covariance-plot-outlier-detection-py"><span class="std std-ref">Outlier detection with several methods.</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers ..."><div class="figure" id="id67">
<img alt="../_images/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png" src="../_images/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="covariance/plot_robust_vs_empirical_covariance.html#sphx-glr-auto-examples-covariance-plot-robust-vs-empirical-covariance-py"><span class="std std-ref">Robust vs Empirical covariance estimate</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="cross-decomposition">
<span id="cross-decomposition-examples"></span><h2>Cross decomposition<a class="headerlink" href="#cross-decomposition" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.cross_decomposition" title="sklearn.cross_decomposition"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_decomposition</span></code></a> module.</p>
<div class="sphx-glr-thumbcontainer" tooltip="Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with mu..."><div class="figure" id="id68">
<img alt="../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" src="../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="cross_decomposition/plot_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py"><span class="std std-ref">Compare cross decomposition methods</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="dataset-examples">
<span id="id1"></span><h2>Dataset examples<a class="headerlink" href="#dataset-examples" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a> module.</p>
<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-..."><div class="figure" id="id69">
<img alt="../_images/sphx_glr_plot_digits_last_image_thumb.png" src="../_images/sphx_glr_plot_digits_last_image_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="datasets/plot_digits_last_image.html#sphx-glr-auto-examples-datasets-plot-digits-last-image-py"><span class="std std-ref">The Digit Dataset</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and P..."><div class="figure" id="id70">
<img alt="../_images/sphx_glr_plot_iris_dataset_thumb.png" src="../_images/sphx_glr_plot_iris_dataset_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="datasets/plot_iris_dataset.html#sphx-glr-auto-examples-datasets-plot-iris-dataset-py"><span class="std std-ref">The Iris Dataset</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Plot several randomly generated 2D classification datasets. This example illustrates the :func:..."><div class="figure" id="id71">
<img alt="../_images/sphx_glr_plot_random_dataset_thumb.png" src="../_images/sphx_glr_plot_random_dataset_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="datasets/plot_random_dataset.html#sphx-glr-auto-examples-datasets-plot-random-dataset-py"><span class="std std-ref">Plot randomly generated classification dataset</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This illustrates the `datasets.make_multilabel_classification` dataset generator. Each sample c..."><div class="figure" id="id72">
<img alt="../_images/sphx_glr_plot_random_multilabel_dataset_thumb.png" src="../_images/sphx_glr_plot_random_multilabel_dataset_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="datasets/plot_random_multilabel_dataset.html#sphx-glr-auto-examples-datasets-plot-random-multilabel-dataset-py"><span class="std std-ref">Plot randomly generated multilabel dataset</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="decomposition">
<span id="decomposition-examples"></span><h2>Decomposition<a class="headerlink" href="#decomposition" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.decomposition</span></code></a> module.</p>
<div class="sphx-glr-thumbcontainer" tooltip="A plot that compares the various Beta-divergence loss functions supported by the Multiplicative..."><div class="figure" id="id73">
<img alt="../_images/sphx_glr_plot_beta_divergence_thumb.png" src="../_images/sphx_glr_plot_beta_divergence_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_beta_divergence.html#sphx-glr-auto-examples-decomposition-plot-beta-divergence-py"><span class="std std-ref">Beta-divergence loss functions</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Principal Component Analysis applied to the Iris dataset."><div class="figure" id="id74">
<img alt="../_images/sphx_glr_plot_pca_iris_thumb.png" src="../_images/sphx_glr_plot_pca_iris_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">PCA example with Iris Data-set</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Incremental principal component analysis (IPCA) is typically used as a replacement for principa..."><div class="figure" id="id75">
<img alt="../_images/sphx_glr_plot_incremental_pca_thumb.png" src="../_images/sphx_glr_plot_incremental_pca_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py"><span class="std std-ref">Incremental PCA</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 a..."><div class="figure" id="id76">
<img alt="../_images/sphx_glr_plot_pca_vs_lda_thumb.png" src="../_images/sphx_glr_plot_pca_vs_lda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py"><span class="std std-ref">Comparison of LDA and PCA 2D projection of Iris dataset</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="An example of estimating sources from noisy data."><div class="figure" id="id77">
<img alt="../_images/sphx_glr_plot_ica_blind_source_separation_thumb.png" src="../_images/sphx_glr_plot_ica_blind_source_separation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py"><span class="std std-ref">Blind source separation using FastICA</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="These figures aid in illustrating how a point cloud can be very flat in one direction--which is..."><div class="figure" id="id78">
<img alt="../_images/sphx_glr_plot_pca_3d_thumb.png" src="../_images/sphx_glr_plot_pca_3d_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_pca_3d.html#sphx-glr-auto-examples-decomposition-plot-pca-3d-py"><span class="std std-ref">Principal components analysis (PCA)</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example illustrates visually in the feature space a comparison by results using two differ..."><div class="figure" id="id79">
<img alt="../_images/sphx_glr_plot_ica_vs_pca_thumb.png" src="../_images/sphx_glr_plot_ica_vs_pca_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_ica_vs_pca.html#sphx-glr-auto-examples-decomposition-plot-ica-vs-pca-py"><span class="std std-ref">FastICA on 2D point clouds</span></a></span></p>
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<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..."><div class="figure" id="id80">
<img alt="../_images/sphx_glr_plot_kernel_pca_thumb.png" src="../_images/sphx_glr_plot_kernel_pca_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py"><span class="std std-ref">Kernel PCA</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Transform a signal as a sparse combination of Ricker wavelets. This example visually compares d..."><div class="figure" id="id81">
<img alt="../_images/sphx_glr_plot_sparse_coding_thumb.png" src="../_images/sphx_glr_plot_sparse_coding_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_sparse_coding.html#sphx-glr-auto-examples-decomposition-plot-sparse-coding-py"><span class="std std-ref">Sparse coding with a precomputed dictionary</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik..."><div class="figure" id="id82">
<img alt="../_images/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png" src="../_images/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py"><span class="std std-ref">Model selection with Probabilistic PCA and Factor Analysis (FA)</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="An example comparing the effect of reconstructing noisy fragments of a raccoon face image using..."><div class="figure" id="id83">
<img alt="../_images/sphx_glr_plot_image_denoising_thumb.png" src="../_images/sphx_glr_plot_image_denoising_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py"><span class="std std-ref">Image denoising using dictionary learning</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example applies to :ref:`olivetti_faces` different unsupervised matrix decomposition (dime..."><div class="figure" id="id84">
<img alt="../_images/sphx_glr_plot_faces_decomposition_thumb.png" src="../_images/sphx_glr_plot_faces_decomposition_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="ensemble-methods">
<span id="ensemble-examples"></span><h2>Ensemble methods<a class="headerlink" href="#ensemble-methods" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.ensemble</span></code></a> module.</p>
<div class="sphx-glr-thumbcontainer" tooltip="A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D sinusoidal dataset with..."><div class="figure" id="id85">
<img alt="../_images/sphx_glr_plot_adaboost_regression_thumb.png" src="../_images/sphx_glr_plot_adaboost_regression_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Decision Tree Regression with AdaBoost</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of forests of trees to evaluate the importance of the pixels in an i..."><div class="figure" id="id86">
<img alt="../_images/sphx_glr_plot_forest_importances_faces_thumb.png" src="../_images/sphx_glr_plot_forest_importances_faces_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_forest_importances_faces.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-faces-py"><span class="std std-ref">Pixel importances with a parallel forest of trees</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This examples shows the use of forests of trees to evaluate the importance of features on an ar..."><div class="figure" id="id87">
<img alt="../_images/sphx_glr_plot_forest_importances_thumb.png" src="../_images/sphx_glr_plot_forest_importances_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py"><span class="std std-ref">Feature importances with forests of trees</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="An example using IsolationForest for anomaly detection."><div class="figure" id="id88">
<img alt="../_images/sphx_glr_plot_isolation_forest_thumb.png" src="../_images/sphx_glr_plot_isolation_forest_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a `VotingClassifier` for two features of the Iris dataset."><div class="figure" id="id89">
<img alt="../_images/sphx_glr_plot_voting_decision_regions_thumb.png" src="../_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="An example to compare multi-output regression with random forest and the :ref:`multioutput.Mult..."><div class="figure" id="id90">
<img alt="../_images/sphx_glr_plot_random_forest_regression_multioutput_thumb.png" src="../_images/sphx_glr_plot_random_forest_regression_multioutput_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py"><span class="std std-ref">Comparing random forests and the multi-output meta estimator</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example shows how quantile regression can be used to create prediction intervals. "><div class="figure" id="id91">
<img alt="../_images/sphx_glr_plot_gradient_boosting_quantile_thumb.png" src="../_images/sphx_glr_plot_gradient_boosting_quantile_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py"><span class="std std-ref">Prediction Intervals for Gradient Boosting Regression</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Illustration of the effect of different regularization strategies for Gradient Boosting. The ex..."><div class="figure" id="id92">
<img alt="../_images/sphx_glr_plot_gradient_boosting_regularization_thumb.png" src="../_images/sphx_glr_plot_gradient_boosting_regularization_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py"><span class="std std-ref">Gradient Boosting regularization</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different ..."><div class="figure" id="id93">
<img alt="../_images/sphx_glr_plot_voting_probas_thumb.png" src="../_images/sphx_glr_plot_voting_probas_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Demonstrate Gradient Boosting on the Boston housing dataset."><div class="figure" id="id94">
<img alt="../_images/sphx_glr_plot_gradient_boosting_regression_thumb.png" src="../_images/sphx_glr_plot_gradient_boosting_regression_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py"><span class="std std-ref">Gradient Boosting regression</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where each new tree is..."><div class="figure" id="id95">
<img alt="../_images/sphx_glr_plot_ensemble_oob_thumb.png" src="../_images/sphx_glr_plot_ensemble_oob_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py"><span class="std std-ref">OOB Errors for Random Forests</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example fits an AdaBoosted decision stump on a non-linearly separable classification datas..."><div class="figure" id="id96">
<img alt="../_images/sphx_glr_plot_adaboost_twoclass_thumb.png" src="../_images/sphx_glr_plot_adaboost_twoclass_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representati..."><div class="figure" id="id97">
<img alt="../_images/sphx_glr_plot_random_forest_embedding_thumb.png" src="../_images/sphx_glr_plot_random_forest_embedding_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_random_forest_embedding.html#sphx-glr-auto-examples-ensemble-plot-random-forest-embedding-py"><span class="std std-ref">Hashing feature transformation using Totally Random Trees</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Partial dependence plots show the dependence between the target function [2]_ and a set of 'tar..."><div class="figure" id="id98">
<img alt="../_images/sphx_glr_plot_partial_dependence_thumb.png" src="../_images/sphx_glr_plot_partial_dependence_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_partial_dependence.html#sphx-glr-auto-examples-ensemble-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence Plots</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example is based on Figure 10.2 from Hastie et al 2009 [1]_ and illustrates the difference..."><div class="figure" id="id99">
<img alt="../_images/sphx_glr_plot_adaboost_hastie_10_2_thumb.png" src="../_images/sphx_glr_plot_adaboost_hastie_10_2_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py"><span class="std std-ref">Discrete versus Real AdaBoost</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example reproduces Figure 1 of Zhu et al [1]_ and shows how boosting can improve predictio..."><div class="figure" id="id100">
<img alt="../_images/sphx_glr_plot_adaboost_multiclass_thumb.png" src="../_images/sphx_glr_plot_adaboost_multiclass_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Gradient boosting is an ensembling technique where several weak learners (regression trees) are..."><div class="figure" id="id101">
<img alt="../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" src="../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">Early stopping of Gradient Boosting</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on t..."><div class="figure" id="id102">
<img alt="../_images/sphx_glr_plot_feature_transformation_thumb.png" src="../_images/sphx_glr_plot_feature_transformation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Out-of-bag (OOB) estimates can be a useful heuristic to estimate the "optimal" number of boosti..."><div class="figure" id="id103">
<img alt="../_images/sphx_glr_plot_gradient_boosting_oob_thumb.png" src="../_images/sphx_glr_plot_gradient_boosting_oob_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py"><span class="std std-ref">Gradient Boosting Out-of-Bag estimates</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares the bias-variance decomposition of the expected mean squa..."><div class="figure" id="id104">
<img alt="../_images/sphx_glr_plot_bias_variance_thumb.png" src="../_images/sphx_glr_plot_bias_variance_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_bias_variance.html#sphx-glr-auto-examples-ensemble-plot-bias-variance-py"><span class="std std-ref">Single estimator versus bagging: bias-variance decomposition</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the i..."><div class="figure" id="id105">
<img alt="../_images/sphx_glr_plot_forest_iris_thumb.png" src="../_images/sphx_glr_plot_forest_iris_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="tutorial-exercises">
<h2>Tutorial exercises<a class="headerlink" href="#tutorial-exercises" title="Permalink to this headline">¶</a></h2>
<p>Exercises for the tutorials</p>
<div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise regarding the use of classification techniques on the Digits dataset."><div class="figure" id="id106">
<img alt="../_images/sphx_glr_plot_digits_classification_exercise_thumb.png" src="../_images/sphx_glr_plot_digits_classification_exercise_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="exercises/plot_digits_classification_exercise.html#sphx-glr-auto-examples-exercises-plot-digits-classification-exercise-py"><span class="std std-ref">Digits Classification Exercise</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise using Cross-validation with an SVM on the Digits dataset."><div class="figure" id="id107">
<img alt="../_images/sphx_glr_plot_cv_digits_thumb.png" src="../_images/sphx_glr_plot_cv_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="exercises/plot_cv_digits.html#sphx-glr-auto-examples-exercises-plot-cv-digits-py"><span class="std std-ref">Cross-validation on Digits Dataset Exercise</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise for using different SVM kernels."><div class="figure" id="id108">
<img alt="../_images/sphx_glr_plot_iris_exercise_thumb.png" src="../_images/sphx_glr_plot_iris_exercise_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="exercises/plot_iris_exercise.html#sphx-glr-auto-examples-exercises-plot-iris-exercise-py"><span class="std std-ref">SVM Exercise</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise which uses cross-validation with linear models."><div class="figure" id="id109">
<img alt="../_images/sphx_glr_plot_cv_diabetes_thumb.png" src="../_images/sphx_glr_plot_cv_diabetes_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="exercises/plot_cv_diabetes.html#sphx-glr-auto-examples-exercises-plot-cv-diabetes-py"><span class="std std-ref">Cross-validation on diabetes Dataset Exercise</span></a></span></p>
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<div style='clear:both'></div></div>
<div class="section" id="feature-selection">
<span id="feature-selection-examples"></span><h2>Feature Selection<a class="headerlink" href="#feature-selection" title="Permalink to this headline">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.feature_selection</span></code></a> module.</p>
<div class="sphx-glr-thumbcontainer" tooltip="A recursive feature elimination example showing the relevance of pixels in a digit classificati..."><div class="figure" id="id110">
<img alt="../_images/sphx_glr_plot_rfe_digits_thumb.png" src="../_images/sphx_glr_plot_rfe_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py"><span class="std std-ref">Recursive feature elimination</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the differences between univariate F-test statistics and mutual inform..."><div class="figure" id="id111">
<img alt="../_images/sphx_glr_plot_f_test_vs_mi_thumb.png" src="../_images/sphx_glr_plot_f_test_vs_mi_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="feature_selection/plot_f_test_vs_mi.html#sphx-glr-auto-examples-feature-selection-plot-f-test-vs-mi-py"><span class="std std-ref">Comparison of F-test and mutual information</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Simple usage of Pipeline that runs successively a univariate feature selection with anova and t..."><div class="figure" id="id112">
<img alt="../_images/sphx_glr_plot_feature_selection_pipeline_thumb.png" src="../_images/sphx_glr_plot_feature_selection_pipeline_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py"><span class="std std-ref">Pipeline Anova SVM</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="A recursive feature elimination example with automatic tuning of the number of features selecte..."><div class="figure" id="id113">
<img alt="../_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" src="../_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py"><span class="std std-ref">Recursive feature elimination with cross-validation</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="Use SelectFromModel meta-transformer along with Lasso to select the best couple of features fro..."><div class="figure" id="id114">
<img alt="../_images/sphx_glr_plot_select_from_model_boston_thumb.png" src="../_images/sphx_glr_plot_select_from_model_boston_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="feature_selection/plot_select_from_model_boston.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-boston-py"><span class="std std-ref">Feature selection using SelectFromModel and LassoCV</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="In order to test if a classification score is significative a technique in repeating the classi..."><div class="figure" id="id115">
<img alt="../_images/sphx_glr_plot_permutation_test_for_classification_thumb.png" src="../_images/sphx_glr_plot_permutation_test_for_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="feature_selection/plot_permutation_test_for_classification.html#sphx-glr-auto-examples-feature-selection-plot-permutation-test-for-classification-py"><span class="std std-ref">Test with permutations the significance of a classification score</span></a></span></p>
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<div class="sphx-glr-thumbcontainer" tooltip="An example showing univariate feature selection."><div class="figure" id="id116">
<img alt="../_images/sphx_glr_plot_feature_selection_thumb.png" src="../_images/sphx_glr_plot_feature_selection_thumb.png" />