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class="bodywrapper"> <div class="body"> <div class="section" id="recursive-feature-elimination-with-cross-validation"> <span id="example-plot-rfe-with-cross-validation-py"></span><h1>Recursive feature elimination with cross-validation<a class="headerlink" href="#recursive-feature-elimination-with-cross-validation" title="Permalink to this headline">ΒΆ</a></h1> <p>Recursive feature elimination with automatic tuning of the number of features selected with cross-validation</p> <img alt="auto_examples/images/plot_rfe_with_cross_validation.png" class="align-center" src="auto_examples/images/plot_rfe_with_cross_validation.png" /> <p><strong>Python source code:</strong> <a class="reference download internal" href="../_downloads/plot_rfe_with_cross_validation.py"><tt class="xref download docutils literal"><span class="pre">plot_rfe_with_cross_validation.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">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</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.cross_val</span> <span class="kn">import</span> <span class="n">StratifiedKFold</span> <span class="kn">from</span> <span class="nn">scikits.learn.feature_selection</span> <span class="kn">import</span> <span class="n">RFECV</span> <span class="kn">from</span> <span class="nn">scikits.learn.datasets</span> <span class="kn">import</span> <span class="n">samples_generator</span> <span class="kn">from</span> <span class="nn">scikits.learn.metrics</span> <span class="kn">import</span> <span class="n">zero_one</span> <span class="c">################################################################################</span> <span class="c"># Loading a dataset</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">samples_generator</span><span class="o">.</span><span class="n">test_dataset_classif</span><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</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">rfecv</span> <span class="o">=</span> <span class="n">RFECV</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">2</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">loss_func</span><span class="o">=</span><span class="n">zero_one</span><span class="p">)</span> <span class="n">rfecv</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">cv</span><span class="o">=</span><span class="n">StratifiedKFold</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="k">print</span> <span class="s">'Optimal number of features : </span><span class="si">%d</span><span class="s">'</span> <span class="o">%</span> <span class="n">rfecv</span><span class="o">.</span><span class="n">support_</span><span class="o">.</span><span class="n">sum</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">figure</span><span class="p">()</span> <span class="n">pl</span><span class="o">.</span><span class="n">semilogx</span><span class="p">(</span><span class="n">rfecv</span><span class="o">.</span><span class="n">n_features_</span><span class="p">,</span> <span class="n">rfecv</span><span class="o">.</span><span class="n">cv_scores_</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">'Number of features selected'</span><span class="p">)</span> <span class="n">pl</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">'Cross validation score (nb of misclassifications)'</span><span class="p">)</span> <span class="c"># 15 ticks regularly-space in log</span> <span class="n">x_ticks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">rfecv</span><span class="o">.</span><span class="n">n_features_</span><span class="o">.</span><span class="n">max</span><span class="p">()),</span> <span class="mi">15</span><span class="p">,</span> <span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span> <span class="n">pl</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x_ticks</span><span class="p">,</span> <span class="n">x_ticks</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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