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<h3>Contents</h3>
<ul>
<li><a class="reference internal" href="#">3.5. Feature selection</a><ul>
<li><a class="reference internal" href="#univariate-feature-selection">3.5.1. Univariate feature selection</a><ul>
<li><a class="reference internal" href="#feature-scoring-functions">3.5.1.1. Feature scoring functions</a><ul>
<li><a class="reference internal" href="#for-classification">3.5.1.1.1. For classification</a></li>
<li><a class="reference internal" href="#for-regression">3.5.1.1.2. For regression</a></li>
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<div class="section" id="feature-selection">
<span id="feature-selection-doc"></span><h1>3.5. Feature selection<a class="headerlink" href="#feature-selection" title="Permalink to this headline">¶</a></h1>
<div class="section" id="univariate-feature-selection">
<h2>3.5.1. Univariate feature selection<a class="headerlink" href="#univariate-feature-selection" title="Permalink to this headline">¶</a></h2>
<p>Univariate feature selection works by selecting the best features based on
univariate statistical tests. It can seen as a preprocessing step
to an estimator. The <cite>scikit.learn</cite> exposes feature selection routines
a objects that implement the <cite>transform</cite> method. The k-best features
can be selected based on:</p>
<p>or by setting a percentile of features to keep using</p>
<p>or using common statistical quantities:</p>
<p>These objects take as input a scoring function that returns
univariate p-values.</p>
<div class="topic">
<p class="topic-title first">Examples:</p>
<p><a class="reference internal" href="../auto_examples/plot_feature_selection.html#example-plot-feature-selection-py"><em>Univariate Feature Selection</em></a></p>
</div>
<div class="section" id="feature-scoring-functions">
<h3>3.5.1.1. Feature scoring functions<a class="headerlink" href="#feature-scoring-functions" title="Permalink to this headline">¶</a></h3>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">Beware not to use a regression scoring function with a classification problem.</p>
</div>
<div class="section" id="for-classification">
<h4>3.5.1.1.1. For classification<a class="headerlink" href="#for-classification" title="Permalink to this headline">¶</a></h4>
</div>
<div class="section" id="for-regression">
<h4>3.5.1.1.2. For regression<a class="headerlink" href="#for-regression" title="Permalink to this headline">¶</a></h4>
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