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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>.oas</a></li>
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<div class="section" id="sklearn-covariance-oas">
<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>.oas<a class="headerlink" href="#sklearn-covariance-oas" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="sklearn.covariance.oas">
<code class="sig-prename descclassname">sklearn.covariance.</code><code class="sig-name descname">oas</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">assume_centered</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/covariance/_shrunk_covariance.py#L434"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.oas" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimate covariance with the Oracle Approximating Shrinkage algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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 covariance estimate.</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>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>shrunk_cov</strong><span class="classifier">array-like of shape (n_features, n_features)</span></dt><dd><p>Shrunk covariance.</p>
</dd>
<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 regularised (shrunk) covariance is:</p>
<p>(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)</p>
<p>where mu = trace(cov) / n_features</p>
<p>The formula we used to implement the OAS is slightly modified compared
to the one given in the article. See <a class="reference internal" href="sklearn.covariance.OAS.html#sklearn.covariance.OAS" title="sklearn.covariance.OAS"><code class="xref py py-class docutils literal notranslate"><span class="pre">OAS</span></code></a> for more details.</p>
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