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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.make_blobs</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.make_blobs"><code class="docutils literal notranslate"><span class="pre">make_blobs</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-blobs">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_blobs</span></code></a></li>
</ul>
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</ul>

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  <section id="sklearn-datasets-make-blobs">
<h1><a class="reference internal" href="../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>.make_blobs<a class="headerlink" href="#sklearn-datasets-make-blobs" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.make_blobs">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_blobs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_box</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(-10.0,</span> <span class="pre">10.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/datasets/_samples_generator.py#L864"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_blobs" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate isotropic Gaussian blobs for clustering.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>n_samples</strong><span class="classifier">int or array-like, default=100</span></dt><dd><p>If int, it is the total number of points equally divided among
clusters.
If array-like, each element of the sequence indicates
the number of samples per cluster.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version v0.20: </span>one can now pass an array-like to the <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> parameter</p>
</div>
</dd>
<dt><strong>n_features</strong><span class="classifier">int, default=2</span></dt><dd><p>The number of features for each sample.</p>
</dd>
<dt><strong>centers</strong><span class="classifier">int or array-like of shape (n_centers, n_features), default=None</span></dt><dd><p>The number of centers to generate, or the fixed center locations.
If n_samples is an int and centers is None, 3 centers are generated.
If n_samples is array-like, centers must be
either None or an array of length equal to the length of n_samples.</p>
</dd>
<dt><strong>cluster_std</strong><span class="classifier">float or array-like of float, default=1.0</span></dt><dd><p>The standard deviation of the clusters.</p>
</dd>
<dt><strong>center_box</strong><span class="classifier">tuple of float (min, max), default=(-10.0, 10.0)</span></dt><dd><p>The bounding box for each cluster center when centers are
generated at random.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><p>Shuffle the samples.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>return_centers</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, then return the centers of each cluster.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.23.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>The generated samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The integer labels for cluster membership of each sample.</p>
</dd>
<dt><strong>centers</strong><span class="classifier">ndarray of shape (n_centers, n_features)</span></dt><dd><p>The centers of each cluster. Only returned if
<code class="docutils literal notranslate"><span class="pre">return_centers=True</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_classification</span></code></a></dt><dd><p>A more intricate variant.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="gp">... </span>                  <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(10, 2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span>
<span class="go">array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">centers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="gp">... </span>                  <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(10, 2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span>
<span class="go">array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])</span>
</pre></div>
</div>
</dd></dl>

<section id="examples-using-sklearn-datasets-make-blobs">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_blobs</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-blobs" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><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="../../auto_examples/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 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" />
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  <div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.23</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="../../auto_examples/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>
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<p><a class="reference internal" href="../../auto_examples/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 class="sphx-glr-thumbcontainer" tooltip="This example illustrates how the Ledoit-Wolf and Oracle Shrinkage Approximating (OAS) estimator..."><img alt="" src="../../_images/sphx_glr_plot_lda_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/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="Reference:"><img alt="" src="../../_images/sphx_glr_plot_mean_shift_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/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="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="../../auto_examples/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="../../auto_examples/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="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="../../auto_examples/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 &quot;int..."><img alt="" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/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="../../auto_examples/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="../../auto_examples/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="../../auto_examples/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="../../auto_examples/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="Reference: Brendan J. Frey and Delbert Dueck, &quot;Clustering by Passing Messages Between Data Poin..."><img alt="" src="../../_images/sphx_glr_plot_affinity_propagation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/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="../../auto_examples/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="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="../../auto_examples/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="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="../../auto_examples/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="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="../../auto_examples/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="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="../../auto_examples/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="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="../../auto_examples/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="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="../../auto_examples/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="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="../../auto_examples/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="This example presents the different strategies implemented in KBinsDiscretizer:"><img alt="" src="../../_images/sphx_glr_plot_discretization_strategies_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_discretization_strategies.html#sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py"><span class="std std-ref">Demonstrating the different strategies of KBinsDiscretizer</span></a></p>
  <div class="sphx-glr-thumbnail-title">Demonstrating the different strategies of KBinsDiscretizer</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vecto..."><img alt="" src="../../_images/sphx_glr_plot_linearsvc_support_vectors_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_linearsvc_support_vectors.html#sphx-glr-auto-examples-svm-plot-linearsvc-support-vectors-py"><span class="std std-ref">Plot the support vectors in LinearSVC</span></a></p>
  <div class="sphx-glr-thumbnail-title">Plot the support vectors in LinearSVC</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The two plots differ only in the area in the middle where the classes are tied. If break_ties=F..."><img alt="" src="../../_images/sphx_glr_plot_svm_tie_breaking_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_svm_tie_breaking.html#sphx-glr-auto-examples-svm-plot-svm-tie-breaking-py"><span class="std std-ref">SVM Tie Breaking Example</span></a></p>
  <div class="sphx-glr-thumbnail-title">SVM Tie Breaking Example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the maximum margin separating hyperplane within a two-class separable dataset using a Supp..."><img alt="" src="../../_images/sphx_glr_plot_separating_hyperplane_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py"><span class="std std-ref">SVM: Maximum margin separating hyperplane</span></a></p>
  <div class="sphx-glr-thumbnail-title">SVM: Maximum margin separating hyperplane</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Find the optimal separating hyperplane using an SVC for classes that are unbalanced."><img alt="" src="../../_images/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/svm/plot_separating_hyperplane_unbalanced.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py"><span class="std std-ref">SVM: Separating hyperplane for unbalanced classes</span></a></p>
  <div class="sphx-glr-thumbnail-title">SVM: Separating hyperplane for unbalanced classes</div>
</div></div><div class="clearer"></div></section>
</section>


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