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<li><a class="reference internal" href="#">SVM: Separating hyperplane for unbalanced classes</a></li>
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<section class="sphx-glr-example-title" id="svm-separating-hyperplane-for-unbalanced-classes">
<span id="sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py"></span><h1>SVM: Separating hyperplane for unbalanced classes<a class="headerlink" href="#svm-separating-hyperplane-for-unbalanced-classes" title="Link to this heading">¶</a></h1>
<p>Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.</p>
<p>We first find the separating plane with a plain SVC and then plot
(dashed) the separating hyperplane with automatically correction for
unbalanced classes.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This example will also work by replacing <code class="docutils literal notranslate"><span class="pre">SVC(kernel="linear")</span></code>
with <code class="docutils literal notranslate"><span class="pre">SGDClassifier(loss="hinge")</span></code>. Setting the <code class="docutils literal notranslate"><span class="pre">loss</span></code> parameter
of the <a class="reference internal" href="../../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" title="sklearn.linear_model.SGDClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">SGDClassifier</span></code></a> equal to <code class="docutils literal notranslate"><span class="pre">hinge</span></code> will yield behaviour
such as that of a SVC with a linear kernel.</p>
<p>For example try instead of the <code class="docutils literal notranslate"><span class="pre">SVC</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <span class="n">SGDClassifier</span><span class="p">(</span><span class="n">n_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
</pre></div>
</div>
</div>
<img src="../../_images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png" srcset="../../_images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png" alt="plot separating hyperplane unbalanced" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.lines</span> <span class="k">as</span> <span class="nn">mlines</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a>
<span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">DecisionBoundaryDisplay</span>
<span class="c1"># we create two clusters of random points</span>
<span class="n">n_samples_1</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">n_samples_2</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">centers</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]]</span>
<span class="n">clusters_std</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a><span class="p">(</span>
<span class="n">n_samples</span><span class="o">=</span><span class="p">[</span><span class="n">n_samples_1</span><span class="p">,</span> <span class="n">n_samples_2</span><span class="p">],</span>
<span class="n">centers</span><span class="o">=</span><span class="n">centers</span><span class="p">,</span>
<span class="n">cluster_std</span><span class="o">=</span><span class="n">clusters_std</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># fit the model and get the separating hyperplane</span>
<span class="n">clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="c1"># fit the model and get the separating hyperplane using weighted classes</span>
<span class="n">wclf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mi">10</span><span class="p">})</span>
<span class="n">wclf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="c1"># plot the samples</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Paired</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="c1"># plot the decision functions for both classifiers</span>
<span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.gca.html#matplotlib.pyplot.gca" title="matplotlib.pyplot.gca" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">gca</span></a><span class="p">()</span>
<span class="n">disp</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="sklearn.inspection.DecisionBoundaryDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-inspection-DecisionBoundaryDisplay sphx-glr-backref-type-py-method"><span class="n">DecisionBoundaryDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
<span class="n">clf</span><span class="p">,</span>
<span class="n">X</span><span class="p">,</span>
<span class="n">plot_method</span><span class="o">=</span><span class="s2">"contour"</span><span class="p">,</span>
<span class="n">colors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span>
<span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">linestyles</span><span class="o">=</span><span class="p">[</span><span class="s2">"-"</span><span class="p">],</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># plot decision boundary and margins for weighted classes</span>
<span class="n">wdisp</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="sklearn.inspection.DecisionBoundaryDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-inspection-DecisionBoundaryDisplay sphx-glr-backref-type-py-method"><span class="n">DecisionBoundaryDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
<span class="n">wclf</span><span class="p">,</span>
<span class="n">X</span><span class="p">,</span>
<span class="n">plot_method</span><span class="o">=</span><span class="s2">"contour"</span><span class="p">,</span>
<span class="n">colors</span><span class="o">=</span><span class="s2">"r"</span><span class="p">,</span>
<span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">linestyles</span><span class="o">=</span><span class="p">[</span><span class="s2">"-"</span><span class="p">],</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span>
<span class="p">[</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span></a><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"non weighted"</span><span class="p">),</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span></a><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"r"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"weighted"</span><span class="p">),</span>
<span class="p">],</span>
<span class="p">[</span><span class="s2">"non weighted"</span><span class="p">,</span> <span class="s2">"weighted"</span><span class="p">],</span>
<span class="n">loc</span><span class="o">=</span><span class="s2">"upper right"</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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