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<li><a class="reference internal" href="#">Plot different SVM classifiers in the iris dataset</a></li>
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<section class="sphx-glr-example-title" id="plot-different-svm-classifiers-in-the-iris-dataset">
<span id="sphx-glr-auto-examples-svm-plot-iris-svc-py"></span><h1>Plot different SVM classifiers in the iris dataset<a class="headerlink" href="#plot-different-svm-classifiers-in-the-iris-dataset" title="Link to this heading">¶</a></h1>
<p>Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:</p>
<ul class="simple">
<li><p>Sepal length</p></li>
<li><p>Sepal width</p></li>
</ul>
<p>This example shows how to plot the decision surface for four SVM classifiers
with different kernels.</p>
<p>The linear models <code class="docutils literal notranslate"><span class="pre">LinearSVC()</span></code> and <code class="docutils literal notranslate"><span class="pre">SVC(kernel='linear')</span></code> yield slightly
different decision boundaries. This can be a consequence of the following
differences:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">LinearSVC</span></code> minimizes the squared hinge loss while <code class="docutils literal notranslate"><span class="pre">SVC</span></code> minimizes the
regular hinge loss.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LinearSVC</span></code> uses the One-vs-All (also known as One-vs-Rest) multiclass
reduction while <code class="docutils literal notranslate"><span class="pre">SVC</span></code> uses the One-vs-One multiclass reduction.</p></li>
</ul>
<p>Both linear models have linear decision boundaries (intersecting hyperplanes)
while the non-linear kernel models (polynomial or Gaussian RBF) have more
flexible non-linear decision boundaries with shapes that depend on the kind of
kernel and its parameters.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>while plotting the decision function of classifiers for toy 2D
datasets can help get an intuitive understanding of their respective
expressive power, be aware that those intuitions don’t always generalize to
more realistic high-dimensional problems.</p>
</div>
<img src="../../_images/sphx_glr_plot_iris_svc_001.png" srcset="../../_images/sphx_glr_plot_iris_svc_001.png" alt="SVC with linear kernel, LinearSVC (linear kernel), SVC with RBF kernel, SVC with polynomial (degree 3) kernel" 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.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">datasets</span><span class="p">,</span> <span class="n">svm</span>
<span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">DecisionBoundaryDisplay</span>
<span class="c1"># import some data to play with</span>
<span class="n">iris</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span></a><span class="p">()</span>
<span class="c1"># Take the first two features. We could avoid this by using a two-dim dataset</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
<span class="c1"># we create an instance of SVM and fit out data. We do not scale our</span>
<span class="c1"># data since we want to plot the support vectors</span>
<span class="n">C</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="c1"># SVM regularization parameter</span>
<span class="n">models</span> <span class="o">=</span> <span class="p">(</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="n">C</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC" 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">LinearSVC</span></a><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">dual</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">),</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">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">),</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">"poly"</span><span class="p">,</span> <span class="n">degree</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">models</span> <span class="o">=</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="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">models</span><span class="p">)</span>
<span class="c1"># title for the plots</span>
<span class="n">titles</span> <span class="o">=</span> <span class="p">(</span>
<span class="s2">"SVC with linear kernel"</span><span class="p">,</span>
<span class="s2">"LinearSVC (linear kernel)"</span><span class="p">,</span>
<span class="s2">"SVC with RBF kernel"</span><span class="p">,</span>
<span class="s2">"SVC with polynomial (degree 3) kernel"</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Set-up 2x2 grid for plotting.</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">sub</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" 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">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="matplotlib.pyplot.subplots_adjust" 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">subplots_adjust</span></a><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="n">X0</span><span class="p">,</span> <span class="n">X1</span> <span class="o">=</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="k">for</span> <span class="n">clf</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">sub</span><span class="o">.</span><span class="n">flatten</span><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">response_method</span><span class="o">=</span><span class="s2">"predict"</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">coolwarm</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</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="n">xlabel</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">ylabel</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X0</span><span class="p">,</span> <span class="n">X1</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">coolwarm</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</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="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</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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<p class="rubric">Related examples</p>
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<p><a class="reference internal" href="../semi_supervised/plot_semi_supervised_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-versus-svm-iris-py"><span class="std std-ref">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</span></a></p>
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<div class="sphx-glr-thumbnail-title">Plot the support vectors in LinearSVC</div>
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<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>
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