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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.covariance</span></code>.EllipticEnvelope</a><ul>
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<div class="section" id="sklearn-covariance-ellipticenvelope">
<h1><a class="reference internal" href="../classes.html#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.covariance</span></code></a>.EllipticEnvelope<a class="headerlink" href="#sklearn-covariance-ellipticenvelope" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.covariance.EllipticEnvelope">
<em class="property">class </em><code class="descclassname">sklearn.covariance.</code><code class="descname">EllipticEnvelope</code><span class="sig-paren">(</span><em>store_precision=True</em>, <em>assume_centered=False</em>, <em>support_fraction=None</em>, <em>contamination=0.1</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/outlier_detection.py#L112"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope" title="Permalink to this definition">¶</a></dt>
<dd><p>An object for detecting outliers in a Gaussian distributed dataset.</p>
<p>Read more in the <a class="reference internal" href="../outlier_detection.html#outlier-detection"><span>User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>store_precision</strong> : bool</p>
<blockquote>
<div><p>Specify if the estimated precision is stored.</p>
</div></blockquote>
<p><strong>assume_centered</strong> : Boolean</p>
<blockquote>
<div><p>If True, the support of robust location and covariance estimates
is computed, and a covariance estimate is recomputed from it,
without centering the data.
Useful to work with data whose mean is significantly equal to
zero but is not exactly zero.
If False, the robust location and covariance are directly computed
with the FastMCD algorithm without additional treatment.</p>
</div></blockquote>
<p><strong>support_fraction</strong> : float, 0 < support_fraction < 1</p>
<blockquote>
<div><p>The proportion of points to be included in the support of the raw
MCD estimate. Default is <code class="docutils literal"><span class="pre">None</span></code>, which implies that the minimum
value of support_fraction will be used within the algorithm:
<cite>[n_sample + n_features + 1] / 2</cite>.</p>
</div></blockquote>
<p><strong>contamination</strong> : float, 0. < contamination < 0.5</p>
<blockquote>
<div><p>The amount of contamination of the data set, i.e. the proportion
of outliers in the data set.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Attributes:</th><td class="field-body"><p class="first"><strong>`contamination`</strong> : float, 0. < contamination < 0.5</p>
<blockquote>
<div><p>The amount of contamination of the data set, i.e. the proportion of outliers in the data set.</p>
</div></blockquote>
<p><strong>location_</strong> : array-like, shape (n_features,)</p>
<blockquote>
<div><p>Estimated robust location</p>
</div></blockquote>
<p><strong>covariance_</strong> : array-like, shape (n_features, n_features)</p>
<blockquote>
<div><p>Estimated robust covariance matrix</p>
</div></blockquote>
<p><strong>precision_</strong> : array-like, shape (n_features, n_features)</p>
<blockquote>
<div><p>Estimated pseudo inverse matrix.
(stored only if store_precision is True)</p>
</div></blockquote>
<p><strong>support_</strong> : array-like, shape (n_samples,)</p>
<blockquote class="last">
<div><p>A mask of the observations that have been used to compute the
robust estimates of location and shape.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="sklearn.covariance.EmpiricalCovariance.html#sklearn.covariance.EmpiricalCovariance" title="sklearn.covariance.EmpiricalCovariance"><code class="xref py py-obj docutils literal"><span class="pre">EmpiricalCovariance</span></code></a>, <a class="reference internal" href="sklearn.covariance.MinCovDet.html#sklearn.covariance.MinCovDet" title="sklearn.covariance.MinCovDet"><code class="xref py py-obj docutils literal"><span class="pre">MinCovDet</span></code></a></p>
</div>
<p class="rubric">Notes</p>
<p>Outlier detection from covariance estimation may break or not
perform well in high-dimensional settings. In particular, one will
always take care to work with <code class="docutils literal"><span class="pre">n_samples</span> <span class="pre">></span> <span class="pre">n_features</span> <span class="pre">**</span> <span class="pre">2</span></code>.</p>
<p class="rubric">References</p>
<table class="docutils footnote" frame="void" id="id1" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label">[1]</td><td>Rousseeuw, P.J., Van Driessen, K. “A fast algorithm for the minimum
covariance determinant estimator” Technometrics 41(3), 212 (1999)</td></tr>
</tbody>
</table>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.correct_covariance" title="sklearn.covariance.EllipticEnvelope.correct_covariance"><code class="xref py py-obj docutils literal"><span class="pre">correct_covariance</span></code></a>(data)</td>
<td>Apply a correction to raw Minimum Covariance Determinant estimates.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.decision_function" title="sklearn.covariance.EllipticEnvelope.decision_function"><code class="xref py py-obj docutils literal"><span class="pre">decision_function</span></code></a>(X[, raw_values])</td>
<td>Compute the decision function of the given observations.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.error_norm" title="sklearn.covariance.EllipticEnvelope.error_norm"><code class="xref py py-obj docutils literal"><span class="pre">error_norm</span></code></a>(comp_cov[, norm, scaling, squared])</td>
<td>Computes the Mean Squared Error between two covariance estimators.</td>
</tr>
<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code>(X[, y])</td>
<td></td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.get_params" title="sklearn.covariance.EllipticEnvelope.get_params"><code class="xref py py-obj docutils literal"><span class="pre">get_params</span></code></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.get_precision" title="sklearn.covariance.EllipticEnvelope.get_precision"><code class="xref py py-obj docutils literal"><span class="pre">get_precision</span></code></a>()</td>
<td>Getter for the precision matrix.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.mahalanobis" title="sklearn.covariance.EllipticEnvelope.mahalanobis"><code class="xref py py-obj docutils literal"><span class="pre">mahalanobis</span></code></a>(observations)</td>
<td>Computes the squared Mahalanobis distances of given observations.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.predict" title="sklearn.covariance.EllipticEnvelope.predict"><code class="xref py py-obj docutils literal"><span class="pre">predict</span></code></a>(X)</td>
<td>Outlyingness of observations in X according to the fitted model.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.reweight_covariance" title="sklearn.covariance.EllipticEnvelope.reweight_covariance"><code class="xref py py-obj docutils literal"><span class="pre">reweight_covariance</span></code></a>(data)</td>
<td>Re-weight raw Minimum Covariance Determinant estimates.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.score" title="sklearn.covariance.EllipticEnvelope.score"><code class="xref py py-obj docutils literal"><span class="pre">score</span></code></a>(X, y[, sample_weight])</td>
<td>Returns the mean accuracy on the given test data and labels.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.set_params" title="sklearn.covariance.EllipticEnvelope.set_params"><code class="xref py py-obj docutils literal"><span class="pre">set_params</span></code></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>store_precision=True</em>, <em>assume_centered=False</em>, <em>support_fraction=None</em>, <em>contamination=0.1</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/outlier_detection.py#L177"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.correct_covariance">
<code class="descname">correct_covariance</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/robust_covariance.py#L640"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.correct_covariance" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply a correction to raw Minimum Covariance Determinant estimates.</p>
<p>Correction using the empirical correction factor suggested
by Rousseeuw and Van Driessen in <a class="reference internal" href="sklearn.covariance.MinCovDet.html#rouseeuw1984" id="id2">[Rouseeuw1984]</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>data</strong> : array-like, shape (n_samples, n_features)</p>
<blockquote>
<div><p>The data matrix, with p features and n samples.
The data set must be the one which was used to compute
the raw estimates.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>covariance_corrected</strong> : array-like, shape (n_features, n_features)</p>
<blockquote class="last">
<div><p>Corrected robust covariance estimate.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.decision_function">
<code class="descname">decision_function</code><span class="sig-paren">(</span><em>X</em>, <em>raw_values=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/outlier_detection.py#L42"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the decision function of the given observations.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape (n_samples, n_features)</p>
<p><strong>raw_values</strong> : bool</p>
<blockquote>
<div><p>Whether or not to consider raw Mahalanobis distances as the
decision function. Must be False (default) for compatibility
with the others outlier detection tools.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>decision</strong> : array-like, shape (n_samples, )</p>
<blockquote class="last">
<div><p>The values of the decision function for each observations.
It is equal to the Mahalanobis distances if <cite>raw_values</cite>
is True. By default (<code class="docutils literal"><span class="pre">raw_values=True</span></code>), it is equal
to the cubic root of the shifted Mahalanobis distances.
In that case, the threshold for being an outlier is 0, which
ensures a compatibility with other outlier detection tools
such as the One-Class SVM.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.error_norm">
<code class="descname">error_norm</code><span class="sig-paren">(</span><em>comp_cov</em>, <em>norm='frobenius'</em>, <em>scaling=True</em>, <em>squared=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/empirical_covariance_.py#L213"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.error_norm" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the Mean Squared Error between two covariance estimators.
(In the sense of the Frobenius norm).</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>comp_cov</strong> : array-like, shape = [n_features, n_features]</p>
<blockquote>
<div><p>The covariance to compare with.</p>
</div></blockquote>
<p><strong>norm</strong> : str</p>
<blockquote>
<div><p>The type of norm used to compute the error. Available error types:
- ‘frobenius’ (default): sqrt(tr(A^t.A))
- ‘spectral’: sqrt(max(eigenvalues(A^t.A))
where A is the error <code class="docutils literal"><span class="pre">(comp_cov</span> <span class="pre">-</span> <span class="pre">self.covariance_)</span></code>.</p>
</div></blockquote>
<p><strong>scaling</strong> : bool</p>
<blockquote>
<div><p>If True (default), the squared error norm is divided by n_features.
If False, the squared error norm is not rescaled.</p>
</div></blockquote>
<p><strong>squared</strong> : bool</p>
<blockquote>
<div><p>Whether to compute the squared error norm or the error norm.
If True (default), the squared error norm is returned.
If False, the error norm is returned.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>The Mean Squared Error (in the sense of the Frobenius norm) between</strong> :</p>
<p class="last"><strong>`self` and `comp_cov` covariance estimators.</strong> :</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py#L199"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>deep: boolean, optional</strong> :</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.get_precision">
<code class="descname">get_precision</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/empirical_covariance_.py#L140"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_precision" title="Permalink to this definition">¶</a></dt>
<dd><p>Getter for the precision matrix.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>precision_</strong> : array-like,</p>
<blockquote class="last">
<div><p>The precision matrix associated to the current covariance object.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.mahalanobis">
<code class="descname">mahalanobis</code><span class="sig-paren">(</span><em>observations</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/empirical_covariance_.py#L265"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.mahalanobis" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the squared Mahalanobis distances of given observations.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>observations</strong> : array-like, shape = [n_observations, n_features]</p>
<blockquote>
<div><p>The observations, the Mahalanobis distances of the which we
compute. Observations are assumed to be drawn from the same
distribution than the data used in fit.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>mahalanobis_distance</strong> : array, shape = [n_observations,]</p>
<blockquote class="last">
<div><p>Squared Mahalanobis distances of the observations.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/outlier_detection.py#L77"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Outlyingness of observations in X according to the fitted model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = (n_samples, n_features)</p>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>is_outliers</strong> : array, shape = (n_samples, ), dtype = bool</p>
<blockquote>
<div><p>For each observations, tells whether or not it should be considered
as an outlier according to the fitted model.</p>
</div></blockquote>
<p><strong>threshold</strong> : float,</p>
<blockquote class="last">
<div><p>The values of the less outlying point’s decision function.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.reweight_covariance">
<code class="descname">reweight_covariance</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/covariance/robust_covariance.py#L664"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.reweight_covariance" title="Permalink to this definition">¶</a></dt>
<dd><p>Re-weight raw Minimum Covariance Determinant estimates.</p>
<p>Re-weight observations using Rousseeuw’s method (equivalent to
deleting outlying observations from the data set before
computing location and covariance estimates). <a class="reference internal" href="sklearn.covariance.MinCovDet.html#rouseeuw1984" id="id3">[Rouseeuw1984]</a></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>data</strong> : array-like, shape (n_samples, n_features)</p>
<blockquote>
<div><p>The data matrix, with p features and n samples.
The data set must be the one which was used to compute
the raw estimates.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>location_reweighted</strong> : array-like, shape (n_features, )</p>
<blockquote>
<div><p>Re-weighted robust location estimate.</p>
</div></blockquote>
<p><strong>covariance_reweighted</strong> : array-like, shape (n_features, n_features)</p>
<blockquote>
<div><p>Re-weighted robust covariance estimate.</p>
</div></blockquote>
<p><strong>support_reweighted</strong> : array-like, type boolean, shape (n_samples,)</p>
<blockquote class="last">
<div><p>A mask of the observations that have been used to compute
the re-weighted robust location and covariance estimates.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.score">
<code class="descname">score</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py#L285"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the mean accuracy on the given test data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = (n_samples, n_features)</p>
<blockquote>
<div><p>Test samples.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape = (n_samples) or (n_samples, n_outputs)</p>
<blockquote>
<div><p>True labels for X.</p>
</div></blockquote>
<p><strong>sample_weight</strong> : array-like, shape = [n_samples], optional</p>
<blockquote>
<div><p>Sample weights.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>score</strong> : float</p>
<blockquote class="last">
<div><p>Mean accuracy of self.predict(X) wrt. y.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.EllipticEnvelope.set_params">
<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
<code class="docutils literal"><span class="pre"><component>__<parameter></span></code> so that it’s possible to update each
component of a nested object.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>self</strong> :</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-covariance-ellipticenvelope">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.covariance.EllipticEnvelope</span></code><a class="headerlink" href="#examples-using-sklearn-covariance-ellipticenvelope" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is us..."><div class="figure" id="id4">
<a class="reference external image-reference" href="./../../auto_examples/applications/plot_outlier_detection_housing.html"><img alt="../../_images/plot_outlier_detection_housing1.png" src="../../_images/plot_outlier_detection_housing1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/plot_outlier_detection_housing.html#example-applications-plot-outlier-detection-housing-py"><span>Outlier detection on a real data set</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="When the amount of contamination is known, this example illustrates two different ways of perfo..."><div class="figure" id="id5">
<a class="reference external image-reference" href="./../../auto_examples/covariance/plot_outlier_detection.html"><img alt="../../_images/plot_outlier_detection1.png" src="../../_images/plot_outlier_detection1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/covariance/plot_outlier_detection.html#example-covariance-plot-outlier-detection-py"><span>Outlier detection with several methods.</span></a></span></p>
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