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<li><a class="reference internal" href="#">SVM-Anova: SVM with univariate feature selection</a><ul>
<li><a class="reference internal" href="#load-some-data-to-play-with">Load some data to play with</a></li>
<li><a class="reference internal" href="#create-the-pipeline">Create the pipeline</a></li>
<li><a class="reference internal" href="#plot-the-cross-validation-score-as-a-function-of-percentile-of-features">Plot the cross-validation score as a function of percentile of features</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-svm-plot-svm-anova-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code or to run this example in your browser via JupyterLite or Binder</p>
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<section class="sphx-glr-example-title" id="svm-anova-svm-with-univariate-feature-selection">
<span id="sphx-glr-auto-examples-svm-plot-svm-anova-py"></span><h1>SVM-Anova: SVM with univariate feature selection<a class="headerlink" href="#svm-anova-svm-with-univariate-feature-selection" title="Link to this heading">¶</a></h1>
<p>This example shows how to perform univariate feature selection before running a
SVC (support vector classifier) to improve the classification scores. We use
the iris dataset (4 features) and add 36 non-informative features. We can find
that our model achieves best performance when we select around 10% of features.</p>
<section id="load-some-data-to-play-with">
<h2>Load some data to play with<a class="headerlink" href="#load-some-data-to-play-with" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</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">load_iris</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</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">load_iris</span></a><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Add non-informative features</span>
<span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">((</span><span class="n">X</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">random</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="mi">0</span><span class="p">],</span> <span class="mi">36</span><span class="p">))))</span>
</pre></div>
</div>
</section>
<section id="create-the-pipeline">
<h2>Create the pipeline<a class="headerlink" href="#create-the-pipeline" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_selection.SelectPercentile.html#sklearn.feature_selection.SelectPercentile" title="sklearn.feature_selection.SelectPercentile" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SelectPercentile</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.feature_selection.f_classif.html#sklearn.feature_selection.f_classif" title="sklearn.feature_selection.f_classif" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-function"><span class="n">f_classif</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</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">SVC</span></a>
<span class="c1"># Create a feature-selection transform, a scaler and an instance of SVM that we</span>
<span class="c1"># combine together to have a full-blown estimator</span>
<span class="n">clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Pipeline</span></a><span class="p">(</span>
<span class="p">[</span>
<span class="p">(</span><span class="s2">"anova"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.feature_selection.SelectPercentile.html#sklearn.feature_selection.SelectPercentile" title="sklearn.feature_selection.SelectPercentile" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SelectPercentile</span></a><span class="p">(</span><a href="../../modules/generated/sklearn.feature_selection.f_classif.html#sklearn.feature_selection.f_classif" title="sklearn.feature_selection.f_classif" class="sphx-glr-backref-module-sklearn-feature_selection sphx-glr-backref-type-py-function"><span class="n">f_classif</span></a><span class="p">)),</span>
<span class="p">(</span><span class="s2">"scaler"</span><span class="p">,</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">()),</span>
<span class="p">(</span><span class="s2">"svc"</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">SVC</span></a><span class="p">(</span><span class="n">gamma</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)),</span>
<span class="p">]</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="plot-the-cross-validation-score-as-a-function-of-percentile-of-features">
<h2>Plot the cross-validation score as a function of percentile of features<a class="headerlink" href="#plot-the-cross-validation-score-as-a-function-of-percentile-of-features" title="Link to this heading">¶</a></h2>
<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.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
<span class="n">score_means</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="n">score_stds</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="n">percentiles</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="k">for</span> <span class="n">percentile</span> <span class="ow">in</span> <span class="n">percentiles</span><span class="p">:</span>
<span class="n">clf</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">anova__percentile</span><span class="o">=</span><span class="n">percentile</span><span class="p">)</span>
<span class="n">this_scores</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</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">y</span><span class="p">)</span>
<span class="n">score_means</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="n">score_stds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_scores</span><span class="o">.</span><span class="n">std</span><span class="p">())</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html#matplotlib.pyplot.errorbar" title="matplotlib.pyplot.errorbar" 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">errorbar</span></a><span class="p">(</span><span class="n">percentiles</span><span class="p">,</span> <span class="n">score_means</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="n">score_stds</span><span class="p">))</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" 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">title</span></a><span class="p">(</span><span class="s2">"Performance of the SVM-Anova varying the percentile of features selected"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xticks.html#matplotlib.pyplot.xticks" title="matplotlib.pyplot.xticks" 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">xticks</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" 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">xlabel</span></a><span class="p">(</span><span class="s2">"Percentile"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" 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">ylabel</span></a><span class="p">(</span><span class="s2">"Accuracy Score"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axis.html#matplotlib.pyplot.axis" title="matplotlib.pyplot.axis" 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">axis</span></a><span class="p">(</span><span class="s2">"tight"</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>
</pre></div>
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<img src="../../_images/sphx_glr_plot_svm_anova_001.png" srcset="../../_images/sphx_glr_plot_svm_anova_001.png" alt="Performance of the SVM-Anova varying the percentile of features selected" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.309 seconds)</p>
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<p class="rubric">Related examples</p>
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<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>
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<div class="sphx-glr-thumbnail-title">Plot different SVM classifiers in the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface..."><img alt="" src="../../_images/sphx_glr_plot_custom_kernel_thumb.png" />
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</div><div class="sphx-glr-thumbcontainer" tooltip="A comparison for the decision boundaries generated on the iris dataset by Label Spreading, Self..."><img alt="" src="../../_images/sphx_glr_plot_semi_supervised_versus_svm_iris_thumb.png" />
<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>
<div class="sphx-glr-thumbnail-title">Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset</div>
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