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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>.ledoit_wolf_shrinkage</a><ul>
<li><a class="reference internal" href="#sklearn.covariance.ledoit_wolf_shrinkage"><code class="docutils literal notranslate"><span class="pre">ledoit_wolf_shrinkage</span></code></a></li>
</ul>
</li>
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<section id="sklearn-covariance-ledoit-wolf-shrinkage">
<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>.ledoit_wolf_shrinkage<a class="headerlink" href="#sklearn-covariance-ledoit-wolf-shrinkage" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.covariance.ledoit_wolf_shrinkage">
<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">ledoit_wolf_shrinkage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/covariance/_shrunk_covariance.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.ledoit_wolf_shrinkage" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimate the shrunk Ledoit-Wolf covariance matrix.</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>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Data from which to compute the Ledoit-Wolf shrunk covariance shrinkage.</p>
</dd>
<dt><strong>assume_centered</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, data will not be centered before computation.
Useful to work with data whose mean is significantly equal to
zero but is not exactly zero.
If False, data will be centered before computation.</p>
</dd>
<dt><strong>block_size</strong><span class="classifier">int, default=1000</span></dt><dd><p>Size of blocks into which the covariance matrix will be split.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>shrinkage</strong><span class="classifier">float</span></dt><dd><p>Coefficient in the convex combination used for the computation
of the shrunk estimate.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The regularized (shrunk) covariance is:</p>
<p>(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)</p>
<p>where mu = trace(cov) / n_features</p>
</dd></dl>
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