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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.fetch_lfw_pairs</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.fetch_lfw_pairs"><code class="docutils literal notranslate"><span class="pre">fetch_lfw_pairs</span></code></a></li>
</ul>
</li>
</ul>

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  <section id="sklearn-datasets-fetch-lfw-pairs">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.fetch_lfw_pairs<a class="headerlink" href="#sklearn-datasets-fetch-lfw-pairs" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_lfw_pairs">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lfw_pairs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">funneled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</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">slice_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(slice(70,</span> <span class="pre">195,</span> <span class="pre">None),</span> <span class="pre">slice(78,</span> <span class="pre">172,</span> <span class="pre">None))</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/datasets/_lfw.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_pairs" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).</p>
<p>Download it if necessary.</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>2</p></td>
</tr>
<tr class="row-even"><td><p>Samples total</p></td>
<td><p>13233</p></td>
</tr>
<tr class="row-odd"><td><p>Dimensionality</p></td>
<td><p>5828</p></td>
</tr>
<tr class="row-even"><td><p>Features</p></td>
<td><p>real, between 0 and 255</p></td>
</tr>
</tbody>
</table>
<p>In the official <a class="reference external" href="http://vis-www.cs.umass.edu/lfw/README.txt">README.txt</a> this task is described as the
“Restricted” task.  As I am not sure as to implement the
“Unrestricted” variant correctly, I left it as unsupported for now.</p>
<blockquote>
<div></div></blockquote>
<p>The original images are 250 x 250 pixels, but the default slice and resize
arguments reduce them to 62 x 47.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/real_world.html#labeled-faces-in-the-wild-dataset"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>subset</strong><span class="classifier">{‘train’, ‘test’, ‘10_folds’}, default=’train’</span></dt><dd><p>Select the dataset to load: ‘train’ for the development training
set, ‘test’ for the development test set, and ‘10_folds’ for the
official evaluation set that is meant to be used with a 10-folds
cross validation.</p>
</dd>
<dt><strong>data_home</strong><span class="classifier">str, default=None</span></dt><dd><p>Specify another download and cache folder for the datasets. By
default all scikit-learn data is stored in ‘~/scikit_learn_data’
subfolders.</p>
</dd>
<dt><strong>funneled</strong><span class="classifier">bool, default=True</span></dt><dd><p>Download and use the funneled variant of the dataset.</p>
</dd>
<dt><strong>resize</strong><span class="classifier">float, default=0.5</span></dt><dd><p>Ratio used to resize the each face picture.</p>
</dd>
<dt><strong>color</strong><span class="classifier">bool, default=False</span></dt><dd><p>Keep the 3 RGB channels instead of averaging them to a single
gray level channel. If color is True the shape of the data has
one more dimension than the shape with color = False.</p>
</dd>
<dt><strong>slice_</strong><span class="classifier">tuple of slice, default=(slice(70, 195), slice(78, 172))</span></dt><dd><p>Provide a custom 2D slice (height, width) to extract the
‘interesting’ part of the jpeg files and avoid use statistical
correlation from the background.</p>
</dd>
<dt><strong>download_if_missing</strong><span class="classifier">bool, default=True</span></dt><dd><p>If False, raise an OSError if the data is not locally available
instead of trying to download the data from the source site.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>data</strong><span class="classifier"><a class="reference internal" href="sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Dictionary-like object, with the following attributes.</p>
<dl>
<dt>data<span class="classifier">ndarray of shape (2200, 5828). Shape depends on <code class="docutils literal notranslate"><span class="pre">subset</span></code>.</span></dt><dd><p>Each row corresponds to 2 ravel’d face images
of original size 62 x 47 pixels.
Changing the <code class="docutils literal notranslate"><span class="pre">slice_</span></code>, <code class="docutils literal notranslate"><span class="pre">resize</span></code> or <code class="docutils literal notranslate"><span class="pre">subset</span></code> parameters
will change the shape of the output.</p>
</dd>
<dt>pairs<span class="classifier">ndarray of shape (2200, 2, 62, 47). Shape depends on <code class="docutils literal notranslate"><span class="pre">subset</span></code></span></dt><dd><p>Each row has 2 face images corresponding
to same or different person from the dataset
containing 5749 people. Changing the <code class="docutils literal notranslate"><span class="pre">slice_</span></code>,
<code class="docutils literal notranslate"><span class="pre">resize</span></code> or <code class="docutils literal notranslate"><span class="pre">subset</span></code> parameters will change the shape of the
output.</p>
</dd>
<dt>target<span class="classifier">numpy array of shape (2200,). Shape depends on <code class="docutils literal notranslate"><span class="pre">subset</span></code>.</span></dt><dd><p>Labels associated to each pair of images.
The two label values being different persons or the same person.</p>
</dd>
<dt>target_names<span class="classifier">numpy array of shape (2,)</span></dt><dd><p>Explains the target values of the target array.
0 corresponds to “Different person”, 1 corresponds to “same person”.</p>
</dd>
<dt>DESCR<span class="classifier">str</span></dt><dd><p>Description of the Labeled Faces in the Wild (LFW) dataset.</p>
</dd>
</dl>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

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