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<section id="examples">
<span id="general-examples"></span><h1>Examples<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h1>
<div class="sphx-glr-thumbnails"></div><section id="release-highlights">
<h2>Release Highlights<a class="headerlink" href="#release-highlights" title="Link to this heading">¶</a></h2>
<p>These examples illustrate the main features of the releases of scikit-learn.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.4! Many bug fixes and improvements wer..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_1_4_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_1_4_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-4-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.4</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.4</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.3! Many bug fixes and improvements wer..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_1_3_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_1_3_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-3-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.3</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.3</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.2! Many bug fixes and improvements wer..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_1_2_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_1_2_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-2-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.2</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.2</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.1! Many bug fixes and improvements wer..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_1_1_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_1_1_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-1-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.1</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.1</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are very pleased to announce the release of scikit-learn 1.0! The library has been stable fo..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_1_0_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.0</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.0</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.24! Many bug fixes and improvements we..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_0_24_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_0_24_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-24-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.24</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.24</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.23! Many bug fixes and improvements we..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_0_23_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_0_23_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-23-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.23</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.23</div>
</div><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..."><img alt="" src="../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
<p><a class="reference internal" href="release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.22</div>
</div></div></section>
<section id="biclustering">
<h2>Biclustering<a class="headerlink" href="#biclustering" title="Link to this heading">¶</a></h2>
<p>Examples concerning biclustering techniques.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spe..."><img alt="" src="../_images/sphx_glr_plot_spectral_biclustering_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">A demo of the Spectral Biclustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clus..."><img alt="" src="../_images/sphx_glr_plot_spectral_coclustering_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">A demo of the Spectral Co-Clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset..."><img alt="" src="../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Biclustering documents with the Spectral Co-clustering algorithm</div>
</div></div></section>
<section id="calibration">
<h2>Calibration<a class="headerlink" href="#calibration" title="Link to this heading">¶</a></h2>
<p>Examples illustrating the calibration of predicted probabilities of classifiers.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba..."><img alt="" src="../_images/sphx_glr_plot_compare_calibration_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparison of Calibration of Classifiers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification one often wants to predict not only the class label, but also th..."><img alt="" src="../_images/sphx_glr_plot_calibration_curve_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Probability Calibration curves</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class ..."><img alt="" src="../_images/sphx_glr_plot_calibration_multiclass_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Probability Calibration for 3-class classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification you often want to predict not only the class label, but also the..."><img alt="" src="../_images/sphx_glr_plot_calibration_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Probability calibration of classifiers</div>
</div></div></section>
<section id="classification">
<h2>Classification<a class="headerlink" href="#classification" title="Link to this heading">¶</a></h2>
<p>General examples about classification algorithms.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this ex..."><img alt="" src="../_images/sphx_glr_plot_classifier_comparison_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Classifier comparison</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example plots the covariance ellipsoids of each class and the decision boundary learned by..."><img alt="" src="../_images/sphx_glr_plot_lda_qda_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Linear and Quadratic Discriminant Analysis with covariance ellipsoid</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how the Ledoit-Wolf and Oracle Approximating Shrinkage (OAS) estimator..."><img alt="" src="../_images/sphx_glr_plot_lda_thumb.png" />
<p><a class="reference internal" href="classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py"><span class="std std-ref">Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification</span></a></p>
<div class="sphx-glr-thumbnail-title">Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the classification probability for different classifiers. We use a 3 class dataset, and we..."><img alt="" src="../_images/sphx_glr_plot_classification_probability_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Plot classification probability</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how scikit-learn can be used to recognize images of hand-written digits, fro..."><img alt="" src="../_images/sphx_glr_plot_digits_classification_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Recognizing hand-written digits</div>
</div></div></section>
<section id="clustering">
<h2>Clustering<a class="headerlink" href="#clustering" title="Link to this heading">¶</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 notranslate"><span class="pre">sklearn.cluster</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the various initialization strategies for K-means in terms of runtim..."><img alt="" src="../_images/sphx_glr_plot_kmeans_digits_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">A demo of K-Means clustering on the handwritten digits data</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spa..."><img alt="" src="../_images/sphx_glr_plot_coin_ward_segmentation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">A demo of structured Ward hierarchical clustering on an image of coins</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Reference:"><img alt="" src="../_images/sphx_glr_plot_mean_shift_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">A demo of the mean-shift clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="- a first experiment with fixed "ground truth labels" (and therefore fixed number of classes)..."><img alt="" src="../_images/sphx_glr_plot_adjusted_for_chance_measures_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Adjustment for chance in clustering performance evaluation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the effect of imposing a connectivity graph to capture local structure in th..."><img alt="" src="../_images/sphx_glr_plot_agglomerative_clustering_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Agglomerative clustering with and without structure</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the effect of different metrics on the hierarchical clustering."><img alt="" src="../_images/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Agglomerative clustering with different metrics</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example to show the output of the sklearn.cluster.kmeans_plusplus function for generating in..."><img alt="" src="../_images/sphx_glr_plot_kmeans_plusplus_thumb.png" />
<p><a class="reference internal" href="cluster/plot_kmeans_plusplus.html#sphx-glr-auto-examples-cluster-plot-kmeans-plusplus-py"><span class="std std-ref">An example of K-Means++ initialization</span></a></p>
<div class="sphx-glr-thumbnail-title">An example of K-Means++ initialization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows differences between Regular K-Means algorithm and Bisecting K-Means."><img alt="" src="../_images/sphx_glr_plot_bisect_kmeans_thumb.png" />
<p><a class="reference internal" href="cluster/plot_bisect_kmeans.html#sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"><span class="std std-ref">Bisecting K-Means and Regular K-Means Performance Comparison</span></a></p>
<div class="sphx-glr-thumbnail-title">Bisecting K-Means and Regular K-Means Performance Comparison</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reduci..."><img alt="" src="../_images/sphx_glr_plot_color_quantization_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Color Quantization using K-Means</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares the timing of BIRCH (with and without the global clustering step) and Min..."><img alt="" src="../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Compare BIRCH and MiniBatchKMeans</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are "int..."><img alt="" src="../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparing different clustering algorithms on toy datasets</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different linkage methods for hierarchical clustering on ..."><img alt="" src="../_images/sphx_glr_plot_linkage_comparison_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparing different hierarchical linkage methods on toy datasets</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa..."><img alt="" src="../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core samples in regi..."><img alt="" src="../_images/sphx_glr_plot_dbscan_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Demo of DBSCAN clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this demo we will take a look at cluster.HDBSCAN from the perspective of generalizing the cl..."><img alt="" src="../_images/sphx_glr_plot_hdbscan_thumb.png" />
<p><a class="reference internal" href="cluster/plot_hdbscan.html#sphx-glr-auto-examples-cluster-plot-hdbscan-py"><span class="std std-ref">Demo of HDBSCAN clustering algorithm</span></a></p>
<div class="sphx-glr-thumbnail-title">Demo of HDBSCAN clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Finds core samples of high density and expands clusters from them. This example uses data that ..."><img alt="" src="../_images/sphx_glr_plot_optics_thumb.png" />
<p><a class="reference internal" href="cluster/plot_optics.html#sphx-glr-auto-examples-cluster-plot-optics-py"><span class="std std-ref">Demo of OPTICS clustering algorithm</span></a></p>
<div class="sphx-glr-thumbnail-title">Demo of OPTICS clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Poin..."><img alt="" src="../_images/sphx_glr_plot_affinity_propagation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Demo of affinity propagation clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example is meant to illustrate situations where k-means produces unintuitive and possibly ..."><img alt="" src="../_images/sphx_glr_plot_kmeans_assumptions_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Demonstration of k-means assumptions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Evaluate the ability of k-means initializations strategies to make the algorithm convergence ro..."><img alt="" src="../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Empirical evaluation of the impact of k-means initialization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="These images show how similar features are merged together using feature agglomeration."><img alt="" src="../_images/sphx_glr_plot_digits_agglomeration_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Feature agglomeration</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares 2 dimensionality reduction strategies:"><img alt="" src="../_images/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Feature agglomeration vs. univariate selection</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Example builds a swiss roll dataset and runs hierarchical clustering on their position."><img alt="" src="../_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Hierarchical clustering: structured vs unstructured ward</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Clustering can be expensive, especially when our dataset contains millions of datapoints. Many ..."><img alt="" src="../_images/sphx_glr_plot_inductive_clustering_thumb.png" />
<p><a class="reference internal" href="cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py"><span class="std std-ref">Inductive Clustering</span></a></p>
<div class="sphx-glr-thumbnail-title">Inductive Clustering</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The plot shows:"><img alt="" src="../_images/sphx_glr_plot_cluster_iris_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">K-means Clustering</div>
</div><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..."><img alt="" src="../_images/sphx_glr_plot_dict_face_patches_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Online learning of a dictionary of parts of faces</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot Hierarchical Clustering Dendrogram"><img alt="" src="../_images/sphx_glr_plot_agglomerative_dendrogram_thumb.png" />
<p><a class="reference internal" href="cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py"><span class="std std-ref">Plot Hierarchical Clustering Dendrogram</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot Hierarchical Clustering Dendrogram</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example uses spectral_clustering on a graph created from voxel-to-voxel difference on an i..."><img alt="" src="../_images/sphx_glr_plot_coin_segmentation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Segmenting the picture of greek coins in regions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Silhouette analysis can be used to study the separation distance between the resulting clusters..."><img alt="" src="../_images/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Selecting the number of clusters with silhouette analysis on KMeans clustering</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, an image with connected circles is generated and spectral clustering is used t..."><img alt="" src="../_images/sphx_glr_plot_segmentation_toy_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Spectral clustering for image segmentation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various linkage option for agglomerative clustering on a 2D embedding of the..."><img alt="" src="../_images/sphx_glr_plot_digits_linkage_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Various Agglomerative Clustering on a 2D embedding of digits</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how one can use KBinsDiscretizer to perform vector quantization on a set of ..."><img alt="" src="../_images/sphx_glr_plot_face_compress_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Vector Quantization Example</div>
</div></div></section>
<section id="covariance-estimation">
<h2>Covariance estimation<a class="headerlink" href="#covariance-estimation" title="Link to this heading">¶</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 notranslate"><span class="pre">sklearn.covariance</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and..."><img alt="" src="../_images/sphx_glr_plot_lw_vs_oas_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Ledoit-Wolf vs OAS estimation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows covariance estimation with Mahalanobis distances on Gaussian distributed dat..."><img alt="" src="../_images/sphx_glr_plot_mahalanobis_distances_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Robust covariance estimation and Mahalanobis distances relevance</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers ..."><img alt="" src="../_images/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Robust vs Empirical covariance estimate</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When working with covariance estimation, the usual approach is to use a maximum likelihood esti..."><img alt="" src="../_images/sphx_glr_plot_covariance_estimation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Using the GraphicalLasso estimator to learn a covariance and sparse precision from a small numb..."><img alt="" src="../_images/sphx_glr_plot_sparse_cov_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Sparse inverse covariance estimation</div>
</div></div></section>
<section id="cross-decomposition">
<h2>Cross decomposition<a class="headerlink" href="#cross-decomposition" title="Link to this heading">¶</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 notranslate"><span class="pre">sklearn.cross_decomposition</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Simple usage of various cross decomposition algorithms:"><img alt="" src="../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Compare cross decomposition methods</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares Principal Component Regression (PCR) and Partial Least Squares Regression..."><img alt="" src="../_images/sphx_glr_plot_pcr_vs_pls_thumb.png" />
<p><a class="reference internal" href="cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">Principal Component Regression vs Partial Least Squares Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Principal Component Regression vs Partial Least Squares Regression</div>
</div></div></section>
<section id="dataset-examples">
<h2>Dataset examples<a class="headerlink" href="#dataset-examples" title="Link to this heading">¶</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 notranslate"><span class="pre">sklearn.datasets</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example plots several randomly generated classification datasets. For easy visualization, ..."><img alt="" src="../_images/sphx_glr_plot_random_dataset_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Plot randomly generated classification dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This illustrates the make_multilabel_classification dataset generator. Each sample consists of ..."><img alt="" src="../_images/sphx_glr_plot_random_multilabel_dataset_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Plot randomly generated multilabel dataset</div>
</div><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-..."><img alt="" src="../_images/sphx_glr_plot_digits_last_image_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">The Digit Dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and P..."><img alt="" src="../_images/sphx_glr_plot_iris_dataset_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">The Iris Dataset</div>
</div></div></section>
<section id="decision-trees">
<h2>Decision Trees<a class="headerlink" href="#decision-trees" title="Link to this heading">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.tree" title="sklearn.tree"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.tree</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A 1D regression with decision tree."><img alt="" src="../_images/sphx_glr_plot_tree_regression_thumb.png" />
<p><a class="reference internal" href="tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py"><span class="std std-ref">Decision Tree Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Decision Tree Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example to illustrate multi-output regression with decision tree."><img alt="" src="../_images/sphx_glr_plot_tree_regression_multioutput_thumb.png" />
<p><a class="reference internal" href="tree/plot_tree_regression_multioutput.html#sphx-glr-auto-examples-tree-plot-tree-regression-multioutput-py"><span class="std std-ref">Multi-output Decision Tree Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Multi-output Decision Tree Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surface of a decision tree trained on pairs of features of the iris dataset."><img alt="" src="../_images/sphx_glr_plot_iris_dtc_thumb.png" />
<p><a class="reference internal" href="tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py"><span class="std std-ref">Plot the decision surface of decision trees trained on the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot the decision surface of decision trees trained on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to preven..."><img alt="" src="../_images/sphx_glr_plot_cost_complexity_pruning_thumb.png" />
<p><a class="reference internal" href="tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py"><span class="std std-ref">Post pruning decision trees with cost complexity pruning</span></a></p>
<div class="sphx-glr-thumbnail-title">Post pruning decision trees with cost complexity pruning</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The decision tree structure can be analysed to gain further insight on the relation between the..."><img alt="" src="../_images/sphx_glr_plot_unveil_tree_structure_thumb.png" />
<p><a class="reference internal" href="tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py"><span class="std std-ref">Understanding the decision tree structure</span></a></p>
<div class="sphx-glr-thumbnail-title">Understanding the decision tree structure</div>
</div></div></section>
<section id="decomposition">
<h2>Decomposition<a class="headerlink" href="#decomposition" title="Link to this heading">¶</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 notranslate"><span class="pre">sklearn.decomposition</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="An example of estimating sources from noisy data."><img alt="" src="../_images/sphx_glr_plot_ica_blind_source_separation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Blind source separation using FastICA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 a..."><img alt="" src="../_images/sphx_glr_plot_pca_vs_lda_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparison of LDA and PCA 2D projection of Iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dim..."><img alt="" src="../_images/sphx_glr_plot_faces_decomposition_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Faces dataset decompositions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Investigating the Iris dataset, we see that sepal length, petal length and petal width are high..."><img alt="" src="../_images/sphx_glr_plot_varimax_fa_thumb.png" />
<p><a class="reference internal" href="decomposition/plot_varimax_fa.html#sphx-glr-auto-examples-decomposition-plot-varimax-fa-py"><span class="std std-ref">Factor Analysis (with rotation) to visualize patterns</span></a></p>
<div class="sphx-glr-thumbnail-title">Factor Analysis (with rotation) to visualize patterns</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates visually in the feature space a comparison by results using two differ..."><img alt="" src="../_images/sphx_glr_plot_ica_vs_pca_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">FastICA on 2D point clouds</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing the effect of reconstructing noisy fragments of a raccoon face image using..."><img alt="" src="../_images/sphx_glr_plot_image_denoising_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Image denoising using dictionary learning</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Incremental principal component analysis (IPCA) is typically used as a replacement for principa..."><img alt="" src="../_images/sphx_glr_plot_incremental_pca_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Incremental PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the difference between the Principal Components Analysis (~sklearn.decomposi..."><img alt="" src="../_images/sphx_glr_plot_kernel_pca_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Kernel PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik..."><img alt="" src="../_images/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Model selection with Probabilistic PCA and Factor Analysis (FA)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Principal Component Analysis applied to the Iris dataset."><img alt="" src="../_images/sphx_glr_plot_pca_iris_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">PCA example with Iris Data-set</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Transform a signal as a sparse combination of Ricker wavelets. This example visually compares d..."><img alt="" src="../_images/sphx_glr_plot_sparse_coding_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Sparse coding with a precomputed dictionary</div>
</div></div></section>
<section id="developing-estimators">
<h2>Developing Estimators<a class="headerlink" href="#developing-estimators" title="Link to this heading">¶</a></h2>
<p>Examples concerning the development of Custom Estimator.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The __sklearn_is_fitted__ method is a convention used in scikit-learn for checking whether an e..."><img alt="" src="../_images/sphx_glr_sklearn_is_fitted_thumb.png" />
<p><a class="reference internal" href="developing_estimators/sklearn_is_fitted.html#sphx-glr-auto-examples-developing-estimators-sklearn-is-fitted-py"><span class="std std-ref">__sklearn_is_fitted__ as Developer API</span></a></p>
<div class="sphx-glr-thumbnail-title">__sklearn_is_fitted__ as Developer API</div>
</div></div></section>
<section id="ensemble-methods">
<h2>Ensemble methods<a class="headerlink" href="#ensemble-methods" title="Link to this heading">¶</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 notranslate"><span class="pre">sklearn.ensemble</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the training times and prediction performances of HistGradient..."><img alt="" src="../_images/sphx_glr_plot_gradient_boosting_categorical_thumb.png" />
<p><a class="reference internal" href="ensemble/plot_gradient_boosting_categorical.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-categorical-py"><span class="std std-ref">Categorical Feature Support in Gradient Boosting</span></a></p>
<div class="sphx-glr-thumbnail-title">Categorical Feature Support in Gradient Boosting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Stacking refers to a method to blend estimators. In this strategy, some estimators are individu..."><img alt="" src="../_images/sphx_glr_plot_stack_predictors_thumb.png" />
<p><a class="reference internal" href="ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py"><span class="std std-ref">Combine predictors using stacking</span></a></p>
<div class="sphx-glr-thumbnail-title">Combine predictors using stacking</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the performance of Random Forest (RF) and Histogram Gradient Boostin..."><img alt="" src="../_images/sphx_glr_plot_forest_hist_grad_boosting_comparison_thumb.png" />
<p><a class="reference internal" href="ensemble/plot_forest_hist_grad_boosting_comparison.html#sphx-glr-auto-examples-ensemble-plot-forest-hist-grad-boosting-comparison-py"><span class="std std-ref">Comparing Random Forests and Histogram Gradient Boosting models</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing Random Forests and Histogram Gradient Boosting models</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example to compare multi-output regression with random forest and the multiclass meta-estima..."><img alt="" src="../_images/sphx_glr_plot_random_forest_regression_multioutput_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparing random forests and the multi-output meta estimator</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D sinusoidal dataset with..."><img alt="" src="../_images/sphx_glr_plot_adaboost_regression_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Decision Tree Regression with AdaBoost</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient Boosting is an ensemble technique that combines multiple weak learners, typically deci..."><img alt="" src="../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" />
<p><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 in Gradient Boosting</span></a></p>
<div class="sphx-glr-thumbnail-title">Early stopping in Gradient Boosting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of a forest of trees to evaluate the importance of features on an ar..."><img alt="" src="../_images/sphx_glr_plot_forest_importances_thumb.png" />
<p><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 a forest of trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Feature importances with a forest of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on t..."><img alt="" src="../_images/sphx_glr_plot_feature_transformation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Feature transformations with ensembles of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient Boosting Out-of-Bag estimates"><img alt="" src="../_images/sphx_glr_plot_gradient_boosting_oob_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting Out-of-Bag estimates</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of w..."><img alt="" src="../_images/sphx_glr_plot_gradient_boosting_regression_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Illustration of the effect of different regularization strategies for Gradient Boosting. The ex..."><img alt="" src="../_images/sphx_glr_plot_gradient_boosting_regularization_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting regularization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representati..."><img alt="" src="../_images/sphx_glr_plot_random_forest_embedding_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Hashing feature transformation using Totally Random Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example using IsolationForest for anomaly detection."><img alt="" src="../_images/sphx_glr_plot_isolation_forest_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">IsolationForest example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of monotonic constraints on a gradient boosting estimator."><img alt="" src="../_images/sphx_glr_plot_monotonic_constraints_thumb.png" />
<p><a class="reference internal" href="ensemble/plot_monotonic_constraints.html#sphx-glr-auto-examples-ensemble-plot-monotonic-constraints-py"><span class="std std-ref">Monotonic Constraints</span></a></p>
<div class="sphx-glr-thumbnail-title">Monotonic Constraints</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how boosting can improve the prediction accuracy on a multi-label classifica..."><img alt="" src="../_images/sphx_glr_plot_adaboost_multiclass_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Multi-class AdaBoosted Decision Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit f..."><img alt="" src="../_images/sphx_glr_plot_ensemble_oob_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">OOB Errors for Random Forests</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of a forest of trees to evaluate the impurity based importance of th..."><img alt="" src="../_images/sphx_glr_plot_forest_importances_faces_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Pixel importances with a parallel forest of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different ..."><img alt="" src="../_images/sphx_glr_plot_voting_probas_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the..."><img alt="" src="../_images/sphx_glr_plot_voting_regressor_thumb.png" />
<p><a class="reference internal" href="ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot individual and voting regression predictions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="" src="../_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the i..."><img alt="" src="../_images/sphx_glr_plot_forest_iris_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how quantile regression can be used to create prediction intervals."><img alt="" src="../_images/sphx_glr_plot_gradient_boosting_quantile_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Prediction Intervals for Gradient Boosting Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares the bias-variance decomposition of the expected mean squa..."><img alt="" src="../_images/sphx_glr_plot_bias_variance_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Single estimator versus bagging: bias-variance decomposition</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example fits an AdaBoosted decision stump on a non-linearly separable classification datas..."><img alt="" src="../_images/sphx_glr_plot_adaboost_twoclass_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Two-class AdaBoost</div>
</div></div></section>
<section id="examples-based-on-real-world-datasets">
<h2>Examples based on real world datasets<a class="headerlink" href="#examples-based-on-real-world-datasets" title="Link to this heading">¶</a></h2>
<p>Applications to real world problems with some medium sized datasets or
interactive user interface.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows the reconstruction of an image from a set of parallel projections, acquired ..."><img alt="" src="../_images/sphx_glr_plot_tomography_l1_reconstruction_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", ..."><img alt="" src="../_images/sphx_glr_plot_face_recognition_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Faces recognition example using eigenfaces and SVMs</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KernelPCA to denoise images. In short, we take advantage of the a..."><img alt="" src="../_images/sphx_glr_plot_digits_denoising_thumb.png" />
<p><a class="reference internal" href="applications/plot_digits_denoising.html#sphx-glr-auto-examples-applications-plot-digits-denoising-py"><span class="std std-ref">Image denoising using kernel PCA</span></a></p>
<div class="sphx-glr-thumbnail-title">Image denoising using kernel PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how pandas-engineered lagged features can be used for time series for..."><img alt="" src="../_images/sphx_glr_plot_time_series_lagged_features_thumb.png" />
<p><a class="reference internal" href="applications/plot_time_series_lagged_features.html#sphx-glr-auto-examples-applications-plot-time-series-lagged-features-py"><span class="std std-ref">Lagged features for time series forecasting</span></a></p>
<div class="sphx-glr-thumbnail-title">Lagged features for time series forecasting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create da..."><img alt="" src="../_images/sphx_glr_svm_gui_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Libsvm GUI</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performa..."><img alt="" src="../_images/sphx_glr_plot_model_complexity_influence_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Model Complexity Influence</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how scikit-learn can be used for classification using an out-of-core..."><img alt="" src="../_images/sphx_glr_plot_out_of_core_classification_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Out-of-core classification of text documents</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is us..."><img alt="" src="../_images/sphx_glr_plot_outlier_detection_wine_thumb.png" />
<p><a class="reference internal" href="applications/plot_outlier_detection_wine.html#sphx-glr-auto-examples-applications-plot-outlier-detection-wine-py"><span class="std std-ref">Outlier detection on a real data set</span></a></p>
<div class="sphx-glr-thumbnail-title">Outlier detection on a real data set</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing the prediction latency of various scikit-learn estimators."><img alt="" src="../_images/sphx_glr_plot_prediction_latency_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Prediction Latency</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Modeling species' geographic distributions is an important problem in conservation biology. In ..."><img alt="" src="../_images/sphx_glr_plot_species_distribution_modeling_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Species distribution modeling</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This notebook introduces different strategies to leverage time-related features for a bike shar..."><img alt="" src="../_images/sphx_glr_plot_cyclical_feature_engineering_thumb.png" />
<p><a class="reference internal" href="applications/plot_cyclical_feature_engineering.html#sphx-glr-auto-examples-applications-plot-cyclical-feature-engineering-py"><span class="std std-ref">Time-related feature engineering</span></a></p>
<div class="sphx-glr-thumbnail-title">Time-related feature engineering</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example of applying NMF and LatentDirichletAllocation on a corpus of documents and e..."><img alt="" src="../_images/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example employs several unsupervised learning techniques to extract the stock market struc..."><img alt="" src="../_images/sphx_glr_plot_stock_market_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Visualizing the stock market structure</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A classical way to assert the relative importance of vertices in a graph is to compute the prin..."><img alt="" src="../_images/sphx_glr_wikipedia_principal_eigenvector_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Wikipedia principal eigenvector</div>
</div></div></section>
<section id="feature-selection">
<h2>Feature Selection<a class="headerlink" href="#feature-selection" title="Link to this heading">¶</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 notranslate"><span class="pre">sklearn.feature_selection</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the differences between univariate F-test statistics and mutual inform..."><img alt="" src="../_images/sphx_glr_plot_f_test_vs_mi_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Comparison of F-test and mutual information</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares two approaches for feature selection: SelectFromModel whi..."><img alt="" src="../_images/sphx_glr_plot_select_from_model_diabetes_thumb.png" />
<p><a class="reference internal" href="feature_selection/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py"><span class="std std-ref">Model-based and sequential feature selection</span></a></p>
<div class="sphx-glr-thumbnail-title">Model-based and sequential feature selection</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how a feature selection can be easily integrated within a machine learning p..."><img alt="" src="../_images/sphx_glr_plot_feature_selection_pipeline_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Pipeline ANOVA SVM</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A recursive feature elimination example showing the relevance of pixels in a digit classificati..."><img alt="" src="../_images/sphx_glr_plot_rfe_digits_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Recursive feature elimination</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features s..."><img alt="" src="../_images/sphx_glr_plot_rfe_with_cross_validation_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Recursive feature elimination with cross-validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This notebook is an example of using univariate feature selection to improve classification acc..."><img alt="" src="../_images/sphx_glr_plot_feature_selection_thumb.png" />
<p><a class="reference internal" href="feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py"><span class="std std-ref">Univariate Feature Selection</span></a></p>
<div class="sphx-glr-thumbnail-title">Univariate Feature Selection</div>
</div></div></section>
<section id="gaussian-mixture-models">
<h2>Gaussian Mixture Models<a class="headerlink" href="#gaussian-mixture-models" title="Link to this heading">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.mixture" title="sklearn.mixture"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.mixture</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitt..."><img alt="" src="../_images/sphx_glr_plot_concentration_prior_thumb.png" />
<p><a class="reference internal" href="mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py"><span class="std std-ref">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</span></a></p>
<div class="sphx-glr-thumbnail-title">Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians..."><img alt="" src="../_images/sphx_glr_plot_gmm_pdf_thumb.png" />
<p><a class="reference internal" href="mixture/plot_gmm_pdf.html#sphx-glr-auto-examples-mixture-plot-gmm-pdf-py"><span class="std std-ref">Density Estimation for a Gaussian mixture</span></a></p>
<div class="sphx-glr-thumbnail-title">Density Estimation for a Gaussian mixture</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Examples of the different methods of initialization in Gaussian Mixture Models"><img alt="" src="../_images/sphx_glr_plot_gmm_init_thumb.png" />
<p><a class="reference internal" href="mixture/plot_gmm_init.html#sphx-glr-auto-examples-mixture-plot-gmm-init-py"><span class="std std-ref">GMM Initialization Methods</span></a></p>
<div class="sphx-glr-thumbnail-title">GMM Initialization Methods</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstration of several covariances types for Gaussian mixture models."><img alt="" src="../_images/sphx_glr_plot_gmm_covariances_thumb.png" />
<p><a class="reference internal" href="mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py"><span class="std std-ref">GMM covariances</span></a></p>
<div class="sphx-glr-thumbnail-title">GMM covariances</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisa..."><img alt="" src="../_images/sphx_glr_plot_gmm_thumb.png" />
<p><a class="reference internal" href="mixture/plot_gmm.html#sphx-glr-auto-examples-mixture-plot-gmm-py"><span class="std std-ref">Gaussian Mixture Model Ellipsoids</span></a></p>
<div class="sphx-glr-thumbnail-title">Gaussian Mixture Model Ellipsoids</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows that model selection can be performed with Gaussian Mixture Models (GMM) usi..."><img alt="" src="../_images/sphx_glr_plot_gmm_selection_thumb.png" />
<p><a class="reference internal" href="mixture/plot_gmm_selection.html#sphx-glr-auto-examples-mixture-plot-gmm-selection-py"><span class="std std-ref">Gaussian Mixture Model Selection</span></a></p>
<div class="sphx-glr-thumbnail-title">Gaussian Mixture Model Selection</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the behavior of Gaussian mixture models fit on data that was not samp..."><img alt="" src="../_images/sphx_glr_plot_gmm_sin_thumb.png" />
<p><a class="reference internal" href="mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py"><span class="std std-ref">Gaussian Mixture Model Sine Curve</span></a></p>
<div class="sphx-glr-thumbnail-title">Gaussian Mixture Model Sine Curve</div>
</div></div></section>
<section id="gaussian-process-for-machine-learning">
<h2>Gaussian Process for Machine Learning<a class="headerlink" href="#gaussian-process-for-machine-learning" title="Link to this heading">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.gaussian_process" title="sklearn.gaussian_process"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.gaussian_process</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows the ability of the WhiteKernel to estimate the noise level in the data. More..."><img alt="" src="../_images/sphx_glr_plot_gpr_noisy_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpr_noisy.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-py"><span class="std std-ref">Ability of Gaussian process regression (GPR) to estimate data noise-level</span></a></p>
<div class="sphx-glr-thumbnail-title">Ability of Gaussian process regression (GPR) to estimate data noise-level</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates differences between a kernel ridge regression and a Gaussian process r..."><img alt="" src="../_images/sphx_glr_plot_compare_gpr_krr_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py"><span class="std std-ref">Comparison of kernel ridge and Gaussian process regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of kernel ridge and Gaussian process regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example is based on Section 5.4.3 of "Gaussian Processes for Machine Learning" [RW2006]_. ..."><img alt="" src="../_images/sphx_glr_plot_gpr_co2_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpr_co2.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-co2-py"><span class="std std-ref">Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)</span></a></p>
<div class="sphx-glr-thumbnail-title">Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A simple one-dimensional regression example computed in two different ways:"><img alt="" src="../_images/sphx_glr_plot_gpr_noisy_targets_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py"><span class="std std-ref">Gaussian Processes regression: basic introductory example</span></a></p>
<div class="sphx-glr-thumbnail-title">Gaussian Processes regression: basic introductory example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF ..."><img alt="" src="../_images/sphx_glr_plot_gpc_iris_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py"><span class="std std-ref">Gaussian process classification (GPC) on iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Gaussian process classification (GPC) on iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of Gaussian processes for regression and classification tasks ..."><img alt="" src="../_images/sphx_glr_plot_gpr_on_structured_data_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpr_on_structured_data.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-on-structured-data-py"><span class="std std-ref">Gaussian processes on discrete data structures</span></a></p>
<div class="sphx-glr-thumbnail-title">Gaussian processes on discrete data structures</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and..."><img alt="" src="../_images/sphx_glr_plot_gpc_xor_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpc_xor.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-xor-py"><span class="std std-ref">Illustration of Gaussian process classification (GPC) on the XOR dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Illustration of Gaussian process classification (GPC) on the XOR dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the prior and posterior of a GaussianProcessRegressor with different k..."><img alt="" src="../_images/sphx_glr_plot_gpr_prior_posterior_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpr_prior_posterior.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-prior-posterior-py"><span class="std std-ref">Illustration of prior and posterior Gaussian process for different kernels</span></a></p>
<div class="sphx-glr-thumbnail-title">Illustration of prior and posterior Gaussian process for different kernels</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A two-dimensional classification example showing iso-probability lines for the predicted probab..."><img alt="" src="../_images/sphx_glr_plot_gpc_isoprobability_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpc_isoprobability.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-isoprobability-py"><span class="std std-ref">Iso-probability lines for Gaussian Processes classification (GPC)</span></a></p>
<div class="sphx-glr-thumbnail-title">Iso-probability lines for Gaussian Processes classification (GPC)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the predicted probability of GPC for an RBF kernel with different choi..."><img alt="" src="../_images/sphx_glr_plot_gpc_thumb.png" />
<p><a class="reference internal" href="gaussian_process/plot_gpc.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-py"><span class="std std-ref">Probabilistic predictions with Gaussian process classification (GPC)</span></a></p>
<div class="sphx-glr-thumbnail-title">Probabilistic predictions with Gaussian process classification (GPC)</div>
</div></div></section>
<section id="generalized-linear-models">
<h2>Generalized Linear Models<a class="headerlink" href="#generalized-linear-models" title="Link to this heading">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.linear_model" title="sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example compares two different bayesian regressors:"><img alt="" src="../_images/sphx_glr_plot_ard_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py"><span class="std std-ref">Comparing Linear Bayesian Regressors</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing Linear Bayesian Regressors</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparing various online solvers"><img alt="" src="../_images/sphx_glr_plot_sgd_comparison_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py"><span class="std std-ref">Comparing various online solvers</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing various online solvers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes a Bayesian Ridge Regression of Sinusoids."><img alt="" src="../_images/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_bayesian_ridge_curvefit.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-curvefit-py"><span class="std std-ref">Curve Fitting with Bayesian Ridge Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Curve Fitting with Bayesian Ridge Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a s..."><img alt="" src="../_images/sphx_glr_plot_sgd_early_stopping_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgd_early_stopping.html#sphx-glr-auto-examples-linear-model-plot-sgd-early-stopping-py"><span class="std std-ref">Early stopping of Stochastic Gradient Descent</span></a></p>
<div class="sphx-glr-thumbnail-title">Early stopping of Stochastic Gradient Descent</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The following example shows how to precompute the gram matrix while using weighted samples with..."><img alt="" src="../_images/sphx_glr_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-elastic-net-precomputed-gram-matrix-with-weighted-samples-py"><span class="std std-ref">Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples</span></a></p>
<div class="sphx-glr-thumbnail-title">Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Fit Ridge and HuberRegressor on a dataset with outliers."><img alt="" src="../_images/sphx_glr_plot_huber_vs_ridge_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py"><span class="std std-ref">HuberRegressor vs Ridge on dataset with strong outliers</span></a></p>
<div class="sphx-glr-thumbnail-title">HuberRegressor vs Ridge on dataset with strong outliers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected ..."><img alt="" src="../_images/sphx_glr_plot_multi_task_lasso_support_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_multi_task_lasso_support.html#sphx-glr-auto-examples-linear-model-plot-multi-task-lasso-support-py"><span class="std std-ref">Joint feature selection with multi-task Lasso</span></a></p>
<div class="sphx-glr-thumbnail-title">Joint feature selection with multi-task Lasso</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elast..."><img alt="" src="../_images/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py"><span class="std std-ref">L1 Penalty and Sparsity in Logistic Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">L1 Penalty and Sparsity in Logistic Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The present example compares three l1-based regression models on a synthetic signal obtained fr..."><img alt="" src="../_images/sphx_glr_plot_lasso_and_elasticnet_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py"><span class="std std-ref">L1-based models for Sparse Signals</span></a></p>
<div class="sphx-glr-thumbnail-title">L1-based models for Sparse Signals</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent."><img alt="" src="../_images/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py"><span class="std std-ref">Lasso and Elastic Net</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso and Elastic Net</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example reproduces the example of Fig. 2 of [ZHT2007]_. A LassoLarsIC estimator is fit on ..."><img alt="" src="../_images/sphx_glr_plot_lasso_lars_ic_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_lasso_lars_ic.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-ic-py"><span class="std std-ref">Lasso model selection via information criteria</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso model selection via information criteria</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example focuses on model selection for Lasso models that are linear models with an L1 pena..."><img alt="" src="../_images/sphx_glr_plot_lasso_model_selection_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py"><span class="std std-ref">Lasso model selection: AIC-BIC / cross-validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso model selection: AIC-BIC / cross-validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We show that linear_model.Lasso provides the same results for dense and sparse data and that in..."><img alt="" src="../_images/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py"><span class="std std-ref">Lasso on dense and sparse data</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso on dense and sparse data</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes..."><img alt="" src="../_images/sphx_glr_plot_lasso_lars_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py"><span class="std std-ref">Lasso path using LARS</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso path using LARS</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The coefficients, residual sum of squares and the coefficient of determination are also calcula..."><img alt="" src="../_images/sphx_glr_plot_ols_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py"><span class="std std-ref">Linear Regression Example</span></a></p>
<div class="sphx-glr-thumbnail-title">Linear Regression Example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Show below is a logistic-regression classifiers decision boundaries on the first two dimensions..."><img alt="" src="../_images/sphx_glr_plot_iris_logistic_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py"><span class="std std-ref">Logistic Regression 3-class Classifier</span></a></p>
<div class="sphx-glr-thumbnail-title">Logistic Regression 3-class Classifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Shown in the plot is how the logistic regression would, in this synthetic dataset, classify val..."><img alt="" src="../_images/sphx_glr_plot_logistic_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_logistic.html#sphx-glr-auto-examples-linear-model-plot-logistic-py"><span class="std std-ref">Logistic function</span></a></p>
<div class="sphx-glr-thumbnail-title">Logistic function</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits c..."><img alt="" src="../_images/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classification using multinomial logistic + L1</span></a></p>
<div class="sphx-glr-thumbnail-title">MNIST classification using multinomial logistic + L1</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression to classify doc..."><img alt="" src="../_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"><span class="std std-ref">Multiclass sparse logistic regression on 20newgroups</span></a></p>
<div class="sphx-glr-thumbnail-title">Multiclass sparse logistic regression on 20newgroups</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we fit a linear model with positive constraints on the regression coefficients..."><img alt="" src="../_images/sphx_glr_plot_nnls_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_nnls.html#sphx-glr-auto-examples-linear-model-plot-nnls-py"><span class="std std-ref">Non-negative least squares</span></a></p>
<div class="sphx-glr-thumbnail-title">Non-negative least squares</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to approximate the solution of sklearn.svm.OneClassSVM in the case of an..."><img alt="" src="../_images/sphx_glr_plot_sgdocsvm_vs_ocsvm_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgdocsvm_vs_ocsvm.html#sphx-glr-auto-examples-linear-model-plot-sgdocsvm-vs-ocsvm-py"><span class="std std-ref">One-Class SVM versus One-Class SVM using Stochastic Gradient Descent</span></a></p>
<div class="sphx-glr-thumbnail-title">One-Class SVM versus One-Class SVM using Stochastic Gradient Descent</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Ridge regression is basically minimizing a penalised version of the least-squared function. The..."><img alt="" src="../_images/sphx_glr_plot_ols_ridge_variance_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_ols_ridge_variance.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-variance-py"><span class="std std-ref">Ordinary Least Squares and Ridge Regression Variance</span></a></p>
<div class="sphx-glr-thumbnail-title">Ordinary Least Squares and Ridge Regression Variance</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encod..."><img alt="" src="../_images/sphx_glr_plot_omp_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_omp.html#sphx-glr-auto-examples-linear-model-plot-omp-py"><span class="std std-ref">Orthogonal Matching Pursuit</span></a></p>
<div class="sphx-glr-thumbnail-title">Orthogonal Matching Pursuit</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Shows the effect of collinearity in the coefficients of an estimator."><img alt="" src="../_images/sphx_glr_plot_ridge_path_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py"><span class="std std-ref">Plot Ridge coefficients as a function of the regularization</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot Ridge coefficients as a function of the regularization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the ..."><img alt="" src="../_images/sphx_glr_plot_sgd_iris_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgd_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py"><span class="std std-ref">Plot multi-class SGD on the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot multi-class SGD on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corre..."><img alt="" src="../_images/sphx_glr_plot_logistic_multinomial_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py"><span class="std std-ref">Plot multinomial and One-vs-Rest Logistic Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot multinomial and One-vs-Rest Logistic Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of log-linear Poisson regression on the French Motor Third-Par..."><img alt="" src="../_images/sphx_glr_plot_poisson_regression_non_normal_loss_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a></p>
<div class="sphx-glr-thumbnail-title">Poisson regression and non-normal loss</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to approximate a function with polynomials up to degree degree by..."><img alt="" src="../_images/sphx_glr_plot_polynomial_interpolation_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py"><span class="std std-ref">Polynomial and Spline interpolation</span></a></p>
<div class="sphx-glr-thumbnail-title">Polynomial and Spline interpolation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how quantile regression can predict non-trivial conditional quantiles."><img alt="" src="../_images/sphx_glr_plot_quantile_regression_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_quantile_regression.html#sphx-glr-auto-examples-linear-model-plot-quantile-regression-py"><span class="std std-ref">Quantile regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Quantile regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" Train l1-penalized logistic regression models on a binary classification problem derived from ..."><img alt="" src="../_images/sphx_glr_plot_logistic_path_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_logistic_path.html#sphx-glr-auto-examples-linear-model-plot-logistic-path-py"><span class="std std-ref">Regularization path of L1- Logistic Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Regularization path of L1- Logistic Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A model that overfits learns the training data too well, capturing both the underlying patterns..."><img alt="" src="../_images/sphx_glr_plot_ridge_coeffs_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py"><span class="std std-ref">Ridge coefficients as a function of the L2 Regularization</span></a></p>
<div class="sphx-glr-thumbnail-title">Ridge coefficients as a function of the L2 Regularization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here a sine function is fit with a polynomial of order 3, for values close to zero."><img alt="" src="../_images/sphx_glr_plot_robust_fit_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py"><span class="std std-ref">Robust linear estimator fitting</span></a></p>
<div class="sphx-glr-thumbnail-title">Robust linear estimator fitting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we see how to robustly fit a linear model to faulty data using the ransac_regr..."><img alt="" src="../_images/sphx_glr_plot_ransac_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py"><span class="std std-ref">Robust linear model estimation using RANSAC</span></a></p>
<div class="sphx-glr-thumbnail-title">Robust linear model estimation using RANSAC</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the maximum margin separating hyperplane within a two-class separable dataset using a line..."><img alt="" src="../_images/sphx_glr_plot_sgd_separating_hyperplane_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py"><span class="std std-ref">SGD: Maximum margin separating hyperplane</span></a></p>
<div class="sphx-glr-thumbnail-title">SGD: Maximum margin separating hyperplane</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net."><img alt="" src="../_images/sphx_glr_plot_sgd_penalties_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgd_penalties.html#sphx-glr-auto-examples-linear-model-plot-sgd-penalties-py"><span class="std std-ref">SGD: Penalties</span></a></p>
<div class="sphx-glr-thumbnail-title">SGD: Penalties</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot decision function of a weighted dataset, where the size of points is proportional to its w..."><img alt="" src="../_images/sphx_glr_plot_sgd_weighted_samples_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgd_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-sgd-weighted-samples-py"><span class="std std-ref">SGD: Weighted samples</span></a></p>
<div class="sphx-glr-thumbnail-title">SGD: Weighted samples</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A plot that compares the various convex loss functions supported by SGDClassifier ."><img alt="" src="../_images/sphx_glr_plot_sgd_loss_functions_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py"><span class="std std-ref">SGD: convex loss functions</span></a></p>
<div class="sphx-glr-thumbnail-title">SGD: convex loss functions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that alth..."><img alt="" src="../_images/sphx_glr_plot_ols_3d_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_ols_3d.html#sphx-glr-auto-examples-linear-model-plot-ols-3d-py"><span class="std std-ref">Sparsity Example: Fitting only features 1 and 2</span></a></p>
<div class="sphx-glr-thumbnail-title">Sparsity Example: Fitting only features 1 and 2</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes a Theil-Sen Regression on a synthetic dataset."><img alt="" src="../_images/sphx_glr_plot_theilsen_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py"><span class="std std-ref">Theil-Sen Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Theil-Sen Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of Poisson, Gamma and Tweedie regression on the French Motor T..."><img alt="" src="../_images/sphx_glr_plot_tweedie_regression_insurance_claims_thumb.png" />
<p><a class="reference internal" href="linear_model/plot_tweedie_regression_insurance_claims.html#sphx-glr-auto-examples-linear-model-plot-tweedie-regression-insurance-claims-py"><span class="std std-ref">Tweedie regression on insurance claims</span></a></p>
<div class="sphx-glr-thumbnail-title">Tweedie regression on insurance claims</div>
</div></div></section>
<section id="inspection">
<h2>Inspection<a class="headerlink" href="#inspection" title="Link to this heading">¶</a></h2>
<p>Examples related to the <a class="reference internal" href="../modules/classes.html#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In linear models, the target value is modeled as a linear combination of the features (see the ..."><img alt="" src="../_images/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png" />
<p><a class="reference internal" href="inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in the interpretation of coefficients of linear models</span></a></p>
<div class="sphx-glr-thumbnail-title">Common pitfalls in the interpretation of coefficients of linear models</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Machine Learning models are great for measuring statistical associations. Unfortunately, unless..."><img alt="" src="../_images/sphx_glr_plot_causal_interpretation_thumb.png" />
<p><a class="reference internal" href="inspection/plot_causal_interpretation.html#sphx-glr-auto-examples-inspection-plot-causal-interpretation-py"><span class="std std-ref">Failure of Machine Learning to infer causal effects</span></a></p>
<div class="sphx-glr-thumbnail-title">Failure of Machine Learning to infer causal effects</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Partial dependence plots show the dependence between the target function [2]_ and a set of feat..."><img alt="" src="../_images/sphx_glr_plot_partial_dependence_thumb.png" />
<p><a class="reference internal" href="inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence and Individual Conditional Expectation Plots</span></a></p>
<div class="sphx-glr-thumbnail-title">Partial Dependence and Individual Conditional Expectation Plots</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the impurity-based feature importance of RandomForestClassifie..."><img alt="" src="../_images/sphx_glr_plot_permutation_importance_thumb.png" />
<p><a class="reference internal" href="inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a></p>
<div class="sphx-glr-thumbnail-title">Permutation Importance vs Random Forest Feature Importance (MDI)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compute the permutation_importance of the features to a trained RandomFores..."><img alt="" src="../_images/sphx_glr_plot_permutation_importance_multicollinear_thumb.png" />
<p><a class="reference internal" href="inspection/plot_permutation_importance_multicollinear.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-multicollinear-py"><span class="std std-ref">Permutation Importance with Multicollinear or Correlated Features</span></a></p>
<div class="sphx-glr-thumbnail-title">Permutation Importance with Multicollinear or Correlated Features</div>
</div></div></section>
<section id="kernel-approximation">
<h2>Kernel Approximation<a class="headerlink" href="#kernel-approximation" title="Link to this heading">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.kernel_approximation" title="sklearn.kernel_approximation"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_approximation</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of PolynomialCountSketch to efficiently generate polynomial ke..."><img alt="" src="../_images/sphx_glr_plot_scalable_poly_kernels_thumb.png" />
<p><a class="reference internal" href="kernel_approximation/plot_scalable_poly_kernels.html#sphx-glr-auto-examples-kernel-approximation-plot-scalable-poly-kernels-py"><span class="std std-ref">Scalable learning with polynomial kernel approximation</span></a></p>
<div class="sphx-glr-thumbnail-title">Scalable learning with polynomial kernel approximation</div>
</div></div></section>
<section id="manifold-learning">
<h2>Manifold learning<a class="headerlink" href="#manifold-learning" title="Link to this heading">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.manifold" title="sklearn.manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="An illustration of dimensionality reduction on the S-curve dataset with various manifold learni..."><img alt="" src="../_images/sphx_glr_plot_compare_methods_thumb.png" />
<p><a class="reference internal" href="manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py"><span class="std std-ref">Comparison of Manifold Learning methods</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of Manifold Learning methods</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An application of the different manifold techniques on a spherical data-set. Here one can see t..."><img alt="" src="../_images/sphx_glr_plot_manifold_sphere_thumb.png" />
<p><a class="reference internal" href="manifold/plot_manifold_sphere.html#sphx-glr-auto-examples-manifold-plot-manifold-sphere-py"><span class="std std-ref">Manifold Learning methods on a severed sphere</span></a></p>
<div class="sphx-glr-thumbnail-title">Manifold Learning methods on a severed sphere</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We illustrate various embedding techniques on the digits dataset."><img alt="" src="../_images/sphx_glr_plot_lle_digits_thumb.png" />
<p><a class="reference internal" href="manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py"><span class="std std-ref">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</span></a></p>
<div class="sphx-glr-thumbnail-title">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of the metric and non-metric MDS on generated noisy data."><img alt="" src="../_images/sphx_glr_plot_mds_thumb.png" />
<p><a class="reference internal" href="manifold/plot_mds.html#sphx-glr-auto-examples-manifold-plot-mds-py"><span class="std std-ref">Multi-dimensional scaling</span></a></p>
<div class="sphx-glr-thumbnail-title">Multi-dimensional scaling</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Swiss Roll And Swiss-Hole Reduction"><img alt="" src="../_images/sphx_glr_plot_swissroll_thumb.png" />
<p><a class="reference internal" href="manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py"><span class="std std-ref">Swiss Roll And Swiss-Hole Reduction</span></a></p>
<div class="sphx-glr-thumbnail-title">Swiss Roll And Swiss-Hole Reduction</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of t-SNE on the two concentric circles and the S-curve datasets for different p..."><img alt="" src="../_images/sphx_glr_plot_t_sne_perplexity_thumb.png" />
<p><a class="reference internal" href="manifold/plot_t_sne_perplexity.html#sphx-glr-auto-examples-manifold-plot-t-sne-perplexity-py"><span class="std std-ref">t-SNE: The effect of various perplexity values on the shape</span></a></p>
<div class="sphx-glr-thumbnail-title">t-SNE: The effect of various perplexity values on the shape</div>
</div></div></section>
<section id="miscellaneous">
<h2>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Link to this heading">¶</a></h2>
<p>Miscellaneous and introductory examples for scikit-learn.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip=" See also sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py"><img alt="" src="../_images/sphx_glr_plot_partial_dependence_visualization_api_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-partial-dependence-visualization-api-py"><span class="std std-ref">Advanced Plotting With Partial Dependence</span></a></p>
<div class="sphx-glr-thumbnail-title">Advanced Plotting With Partial Dependence</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Da..."><img alt="" src="../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_anomaly_comparison.html#sphx-glr-auto-examples-miscellaneous-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing anomaly detection algorithms for outlier detection on toy datasets</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel ..."><img alt="" src="../_images/sphx_glr_plot_kernel_ridge_regression_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_kernel_ridge_regression.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-ridge-regression-py"><span class="std std-ref">Comparison of kernel ridge regression and SVR</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of kernel ridge regression and SVR</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The default configuration for displaying a pipeline in a Jupyter Notebook is 'diagram' where se..."><img alt="" src="../_images/sphx_glr_plot_pipeline_display_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_pipeline_display.html#sphx-glr-auto-examples-miscellaneous-plot-pipeline-display-py"><span class="std std-ref">Displaying Pipelines</span></a></p>
<div class="sphx-glr-thumbnail-title">Displaying Pipelines</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates different ways estimators and pipelines can be displayed."><img alt="" src="../_images/sphx_glr_plot_estimator_representation_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_estimator_representation.html#sphx-glr-auto-examples-miscellaneous-plot-estimator-representation-py"><span class="std std-ref">Displaying estimators and complex pipelines</span></a></p>
<div class="sphx-glr-thumbnail-title">Displaying estimators and complex pipelines</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two outlier detection algorithms, namely local_outlier_factor (LOF) and i..."><img alt="" src="../_images/sphx_glr_plot_outlier_detection_bench_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_outlier_detection_bench.html#sphx-glr-auto-examples-miscellaneous-plot-outlier-detection-bench-py"><span class="std std-ref">Evaluation of outlier detection estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Evaluation of outlier detection estimators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example illustrating the approximation of the feature map of an RBF kernel."><img alt="" src="../_images/sphx_glr_plot_kernel_approximation_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_kernel_approximation.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a></p>
<div class="sphx-glr-thumbnail-title">Explicit feature map approximation for RBF kernels</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of multi-output estimator to complete images. The goal is to predict..."><img alt="" src="../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_multioutput_face_completion.html#sphx-glr-auto-examples-miscellaneous-plot-multioutput-face-completion-py"><span class="std std-ref">Face completion with a multi-output estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Face completion with a multi-output estimators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example will demonstrate the set_output API to configure transformers to output pandas Dat..."><img alt="" src="../_images/sphx_glr_plot_set_output_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a></p>
<div class="sphx-glr-thumbnail-title">Introducing the set_output API</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of the isotonic regression on generated data (non-linear monotonic trend with h..."><img alt="" src="../_images/sphx_glr_plot_isotonic_regression_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_isotonic_regression.html#sphx-glr-auto-examples-miscellaneous-plot-isotonic-regression-py"><span class="std std-ref">Isotonic Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Isotonic Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This document shows how you can use the metadata routing mechanism <metadata_routing> in scikit..."><img alt="" src="../_images/sphx_glr_plot_metadata_routing_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_metadata_routing.html#sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Metadata Routing</span></a></p>
<div class="sphx-glr-thumbnail-title">Metadata Routing</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example simulates a multi-label document classification problem. The dataset is generated ..."><img alt="" src="../_images/sphx_glr_plot_multilabel_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_multilabel.html#sphx-glr-auto-examples-miscellaneous-plot-multilabel-py"><span class="std std-ref">Multilabel classification</span></a></p>
<div class="sphx-glr-thumbnail-title">Multilabel classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API"><img alt="" src="../_images/sphx_glr_plot_roc_curve_visualization_api_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
<div class="sphx-glr-thumbnail-title">ROC Curve with Visualization API</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly pr..."><img alt="" src="../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></p>
<div class="sphx-glr-thumbnail-title">The Johnson-Lindenstrauss bound for embedding with random projections</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will construct display objects, ConfusionMatrixDisplay, RocCurveDisplay, an..."><img alt="" src="../_images/sphx_glr_plot_display_object_visualization_thumb.png" />
<p><a class="reference internal" href="miscellaneous/plot_display_object_visualization.html#sphx-glr-auto-examples-miscellaneous-plot-display-object-visualization-py"><span class="std std-ref">Visualizations with Display Objects</span></a></p>
<div class="sphx-glr-thumbnail-title">Visualizations with Display Objects</div>
</div></div></section>
<section id="missing-value-imputation">
<h2>Missing Value Imputation<a class="headerlink" href="#missing-value-imputation" title="Link to this heading">¶</a></h2>
<p>Examples concerning the <a class="reference internal" href="../modules/classes.html#module-sklearn.impute" title="sklearn.impute"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.impute</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Missing values can be replaced by the mean, the median or the most frequent value using the bas..."><img alt="" src="../_images/sphx_glr_plot_missing_values_thumb.png" />
<p><a class="reference internal" href="impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a></p>
<div class="sphx-glr-thumbnail-title">Imputing missing values before building an estimator</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The IterativeImputer class is very flexible - it can be used with a variety of estimators to do..."><img alt="" src="../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p><a class="reference internal" href="impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></p>
<div class="sphx-glr-thumbnail-title">Imputing missing values with variants of IterativeImputer</div>
</div></div></section>
<section id="model-selection">
<h2>Model Selection<a class="headerlink" href="#model-selection" title="Link to this heading">¶</a></h2>
<p>Examples related to the <a class="reference internal" href="../modules/classes.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example balances model complexity and cross-validated score by finding a decent accuracy w..."><img alt="" src="../_images/sphx_glr_plot_grid_search_refit_callable_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a></p>
<div class="sphx-glr-thumbnail-title">Balance model complexity and cross-validated score</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the class_likelihood_ratios function, which computes the positive and..."><img alt="" src="../_images/sphx_glr_plot_likelihood_ratios_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_likelihood_ratios.html#sphx-glr-auto-examples-model-selection-plot-likelihood-ratios-py"><span class="std std-ref">Class Likelihood Ratios to measure classification performance</span></a></p>
<div class="sphx-glr-thumbnail-title">Class Likelihood Ratios to measure classification performance</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with S..."><img alt="" src="../_images/sphx_glr_plot_randomized_search_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py"><span class="std std-ref">Comparing randomized search and grid search for hyperparameter estimation</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing randomized search and grid search for hyperparameter estimation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares the parameter search performed by HalvingGridSearchCV and GridSearchCV."><img alt="" src="../_images/sphx_glr_plot_successive_halving_heatmap_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_successive_halving_heatmap.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-heatmap-py"><span class="std std-ref">Comparison between grid search and successive halving</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison between grid search and successive halving</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Example of confusion matrix usage to evaluate the quality of the output of a classifier on the ..."><img alt="" src="../_images/sphx_glr_plot_confusion_matrix_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py"><span class="std std-ref">Confusion matrix</span></a></p>
<div class="sphx-glr-thumbnail-title">Confusion matrix</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the ..."><img alt="" src="../_images/sphx_glr_plot_grid_search_digits_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py"><span class="std std-ref">Custom refit strategy of a grid search with cross-validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Custom refit strategy of a grid search with cross-validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Multiple metric parameter search can be done by setting the scoring parameter to a list of metr..."><img alt="" src="../_images/sphx_glr_plot_multi_metric_evaluation_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py"><span class="std std-ref">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</span></a></p>
<div class="sphx-glr-thumbnail-title">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare two binary classification multi-threshold metrics: the Receiver Ope..."><img alt="" src="../_images/sphx_glr_plot_det_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_det.html#sphx-glr-auto-examples-model-selection-plot-det-py"><span class="std std-ref">Detection error tradeoff (DET) curve</span></a></p>
<div class="sphx-glr-thumbnail-title">Detection error tradeoff (DET) curve</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluat..."><img alt="" src="../_images/sphx_glr_plot_roc_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py"><span class="std std-ref">Multiclass Receiver Operating Characteristic (ROC)</span></a></p>
<div class="sphx-glr-thumbnail-title">Multiclass Receiver Operating Characteristic (ROC)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares non-nested and nested cross-validation strategies on a classifier of the ..."><img alt="" src="../_images/sphx_glr_plot_nested_cross_validation_iris_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py"><span class="std std-ref">Nested versus non-nested cross-validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Nested versus non-nested cross-validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use cross_val_predict together with PredictionErrorDisplay to visuali..."><img alt="" src="../_images/sphx_glr_plot_cv_predict_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py"><span class="std std-ref">Plotting Cross-Validated Predictions</span></a></p>
<div class="sphx-glr-thumbnail-title">Plotting Cross-Validated Predictions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to use the class LearningCurveDisplay to easily plot learning curv..."><img alt="" src="../_images/sphx_glr_plot_learning_curve_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py"><span class="std std-ref">Plotting Learning Curves and Checking Models’ Scalability</span></a></p>
<div class="sphx-glr-thumbnail-title">Plotting Learning Curves and Checking Models' Scalability</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this plot you can see the training scores and validation scores of an SVM for different valu..."><img alt="" src="../_images/sphx_glr_plot_validation_curve_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py"><span class="std std-ref">Plotting Validation Curves</span></a></p>
<div class="sphx-glr-thumbnail-title">Plotting Validation Curves</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Example of Precision-Recall metric to evaluate classifier output quality."><img alt="" src="../_images/sphx_glr_plot_precision_recall_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py"><span class="std std-ref">Precision-Recall</span></a></p>
<div class="sphx-glr-thumbnail-title">Precision-Recall</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example presents how to estimate and visualize the variance of the Receiver Operating Char..."><img alt="" src="../_images/sphx_glr_plot_roc_crossval_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py"><span class="std std-ref">Receiver Operating Characteristic (ROC) with cross validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Receiver Operating Characteristic (ROC) with cross validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is 20newsgroups_dataset which will be automatically downloaded..."><img alt="" src="../_images/sphx_glr_plot_grid_search_text_feature_extraction_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-plot-grid-search-text-feature-extraction-py"><span class="std std-ref">Sample pipeline for text feature extraction and evaluation</span></a></p>
<div class="sphx-glr-thumbnail-title">Sample pipeline for text feature extraction and evaluation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how to statistically compare the performance of models trained and eva..."><img alt="" src="../_images/sphx_glr_plot_grid_search_stats_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_grid_search_stats.html#sphx-glr-auto-examples-model-selection-plot-grid-search-stats-py"><span class="std std-ref">Statistical comparison of models using grid search</span></a></p>
<div class="sphx-glr-thumbnail-title">Statistical comparison of models using grid search</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how a successive halving search (~sklearn.model_selection.HalvingGridS..."><img alt="" src="../_images/sphx_glr_plot_successive_halving_iterations_thumb.png" />
<p><a class="reference internal" href="model_selection/plot_successive_halving_iterations.html#sphx-glr-auto-examples-model-selection-plot-successive-halving-iterations-py"><span class="std std-ref">Successive Halving Iterations</span></a></p>