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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_people</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.fetch_lfw_people"><code class="docutils literal notranslate"><span class="pre">fetch_lfw_people</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-lfw-people">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_lfw_people</span></code></a></li>
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<section id="sklearn-datasets-fetch-lfw-people">
<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_people<a class="headerlink" href="#sklearn-datasets-fetch-lfw-people" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_lfw_people">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_lfw_people</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">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">min_faces_per_person</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</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>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/datasets/_lfw.py#L235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_people" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the Labeled Faces in the Wild (LFW) people 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>5749</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>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>
<dt><strong>data_home</strong><span class="classifier">str or path-like, 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 or None, default=0.5</span></dt><dd><p>Ratio used to resize the each face picture. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, no resizing is
performed.</p>
</dd>
<dt><strong>min_faces_per_person</strong><span class="classifier">int, default=None</span></dt><dd><p>The extracted dataset will only retain pictures of people that have at
least <code class="docutils literal notranslate"><span class="pre">min_faces_per_person</span></code> different pictures.</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>
<dt><strong>return_X_y</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, returns <code class="docutils literal notranslate"><span class="pre">(dataset.data,</span> <span class="pre">dataset.target)</span></code> instead of a Bunch
object. See below for more information about the <code class="docutils literal notranslate"><span class="pre">dataset.data</span></code> and
<code class="docutils literal notranslate"><span class="pre">dataset.target</span></code> object.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>dataset</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 class="simple">
<dt>data<span class="classifier">numpy array of shape (13233, 2914)</span></dt><dd><p>Each row corresponds to a ravelled face image
of original size 62 x 47 pixels.
Changing the <code class="docutils literal notranslate"><span class="pre">slice_</span></code> or resize parameters will change the
shape of the output.</p>
</dd>
<dt>images<span class="classifier">numpy array of shape (13233, 62, 47)</span></dt><dd><p>Each row is a face image corresponding to one of the 5749 people in
the dataset. Changing the <code class="docutils literal notranslate"><span class="pre">slice_</span></code>
or resize parameters will change the shape of the output.</p>
</dd>
<dt>target<span class="classifier">numpy array of shape (13233,)</span></dt><dd><p>Labels associated to each face image.
Those labels range from 0-5748 and correspond to the person IDs.</p>
</dd>
<dt>target_names<span class="classifier">numpy array of shape (5749,)</span></dt><dd><p>Names of all persons in the dataset.
Position in array corresponds to the person ID in the target array.</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>
<dt><strong>(data, target)</strong><span class="classifier">tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True</span></dt><dd><p>A tuple of two ndarray. The first containing a 2D array of
shape (n_samples, n_features) with each row representing one
sample and each column representing the features. The second
ndarray of shape (n_samples,) containing the target samples.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<section id="examples-using-sklearn-datasets-fetch-lfw-people">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_lfw_people</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-lfw-people" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", ..."><img alt="" src="../../_images/sphx_glr_plot_face_recognition_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py"><span class="std std-ref">Faces recognition example using eigenfaces and SVMs</span></a></p>
<div class="sphx-glr-thumbnail-title">Faces recognition example using eigenfaces and SVMs</div>
</div></div><div class="clearer"></div></section>
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