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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code>.shrunk_covariance</a><ul>
<li><a class="reference internal" href="#sklearn.covariance.shrunk_covariance"><code class="docutils literal notranslate"><span class="pre">shrunk_covariance</span></code></a></li>
</ul>
</li>
</ul>
</div>
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<section id="sklearn-covariance-shrunk-covariance">
<h1><a class="reference internal" href="../classes.html#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a>.shrunk_covariance<a class="headerlink" href="#sklearn-covariance-shrunk-covariance" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.covariance.shrunk_covariance">
<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">shrunk_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">emp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shrinkage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/covariance/_shrunk_covariance.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.shrunk_covariance" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate a covariance matrix shrunk on the diagonal.</p>
<p>Read more in the <a class="reference internal" href="../covariance.html#shrunk-covariance"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>emp_cov</strong><span class="classifier">array-like of shape (n_features, n_features)</span></dt><dd><p>Covariance matrix to be shrunk.</p>
</dd>
<dt><strong>shrinkage</strong><span class="classifier">float, default=0.1</span></dt><dd><p>Coefficient in the convex combination used for the computation
of the shrunk estimate. Range is [0, 1].</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>shrunk_cov</strong><span class="classifier">ndarray of shape (n_features, n_features)</span></dt><dd><p>Shrunk covariance.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The regularized (shrunk) covariance is given by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="n">cov</span> <span class="o">+</span> <span class="n">shrinkage</span> <span class="o">*</span> <span class="n">mu</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">n_features</span><span class="p">)</span>
</pre></div>
</div>
<p>where <code class="docutils literal notranslate"><span class="pre">mu</span> <span class="pre">=</span> <span class="pre">trace(cov)</span> <span class="pre">/</span> <span class="pre">n_features</span></code>.</p>
</dd></dl>
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