<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>Recursive feature elimination — scikits.learn v0.6.0 documentation</title> <link rel="stylesheet" href="../_static/nature.css" type="text/css" /> <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> <script type="text/javascript"> var DOCUMENTATION_OPTIONS = { URL_ROOT: '../', VERSION: '0.6.0', COLLAPSE_INDEX: false, FILE_SUFFIX: '.html', HAS_SOURCE: true }; </script> <script type="text/javascript" src="../_static/jquery.js"></script> <script type="text/javascript" src="../_static/underscore.js"></script> <script type="text/javascript" src="../_static/doctools.js"></script> <link rel="shortcut icon" href="../_static/favicon.ico"/> <link rel="author" title="About these documents" href="../about.html" /> <link rel="top" 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class="documentwrapper"> <div class="bodywrapper"> <div class="body"> <div class="section" id="recursive-feature-elimination"> <span id="example-plot-rfe-digits-py"></span><h1>Recursive feature elimination<a class="headerlink" href="#recursive-feature-elimination" title="Permalink to this headline">ΒΆ</a></h1> <p>A recursive feature elimination is performed prior to SVM classification.</p> <img alt="auto_examples/images/plot_rfe_digits.png" class="align-center" src="auto_examples/images/plot_rfe_digits.png" /> <p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/plot_rfe_digits.py"><tt class="xref download docutils literal"><span class="pre">plot_rfe_digits.py</span></tt></a></p> <div class="highlight-python"><div class="highlight"><pre><span class="k">print</span> <span class="n">__doc__</span> <span class="kn">from</span> <span class="nn">scikits.learn.svm</span> <span class="kn">import</span> <span class="n">SVC</span> <span class="kn">from</span> <span class="nn">scikits.learn</span> <span class="kn">import</span> <span class="n">datasets</span> <span class="kn">from</span> <span class="nn">scikits.learn.feature_selection</span> <span class="kn">import</span> <span class="n">RFE</span> <span class="c">################################################################################</span> <span class="c"># Loading the Digits dataset</span> <span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span> <span class="c"># To apply an classifier on this data, we need to flatten the image, to</span> <span class="c"># turn the data in a (samples, feature) matrix:</span> <span class="n">n_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">)</span> <span class="n">X</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">n_samples</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> <span class="n">y</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span> <span class="c">################################################################################</span> <span class="c"># Create the RFE object and compute a cross-validated score</span> <span class="n">svc</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s">"linear"</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="n">rfe</span> <span class="o">=</span> <span class="n">RFE</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">svc</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">percentage</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span> <span class="n">rfe</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="n">image_ranking_</span> <span class="o">=</span> <span class="n">rfe</span><span class="o">.</span><span class="n">ranking_</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="kn">import</span> <span class="nn">pylab</span> <span class="kn">as</span> <span class="nn">pl</span> <span class="n">pl</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">image_ranking_</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span> <span class="n">pl</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">'Ranking of voxels with RFE'</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> </pre></div> </div> </div> </div> </div> </div> <div class="clearer"></div> </div> </div> <div class="footer"> <p style="text-align: center">This documentation is relative to scikits.learn version 0.6.0<p> © 2010, scikits.learn developers (BSD Lincense). 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