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</style><div class="section" id="dataset-loading-utilities">
<span id="datasets"></span><h1><span class="section-number">7. </span>Dataset loading utilities<a class="headerlink" href="#dataset-loading-utilities" title="Permalink to this headline">¶</a></h1>
<p>The <code class="docutils literal notranslate"><span class="pre">sklearn.datasets</span></code> package embeds some small toy datasets
as introduced in the <a class="reference internal" href="tutorial/basic/tutorial.html#loading-example-dataset"><span class="std std-ref">Getting Started</span></a> section.</p>
<p>This package also features helpers to fetch larger datasets commonly
used by the machine learning community to benchmark algorithms on data
that comes from the ‘real world’.</p>
<p>To evaluate the impact of the scale of the dataset (<code class="docutils literal notranslate"><span class="pre">n_samples</span></code> and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code>) while controlling the statistical properties of the data
(typically the correlation and informativeness of the features), it is
also possible to generate synthetic data.</p>
<p><strong>General dataset API.</strong> There are three main kinds of dataset interfaces that
can be used to get datasets depending on the desired type of dataset.</p>
<p><strong>The dataset loaders.</strong> They can be used to load small standard datasets,
described in the <a class="reference internal" href="datasets/toy_dataset.html#toy-datasets"><span class="std std-ref">Toy datasets</span></a> section.</p>
<p><strong>The dataset fetchers.</strong> They can be used to download and load larger datasets,
described in the <a class="reference internal" href="datasets/real_world.html#real-world-datasets"><span class="std std-ref">Real world datasets</span></a> section.</p>
<p>Both loaders and fetchers functions return a <a class="reference internal" href="modules/generated/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>
object holding at least two items:
an array of shape <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> * <code class="docutils literal notranslate"><span class="pre">n_features</span></code> with
key <code class="docutils literal notranslate"><span class="pre">data</span></code> (except for 20newsgroups) and a numpy array of
length <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>, containing the target values, with key <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
<p>The Bunch object is a dictionary that exposes its keys as attributes.
For more information about Bunch object, see <a class="reference internal" href="modules/generated/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>.</p>
<p>It’s also possible for almost all of these function to constrain the output
to be a tuple containing only the data and the target, by setting the
<code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
<p>The datasets also contain a full description in their <code class="docutils literal notranslate"><span class="pre">DESCR</span></code> attribute and
some contain <code class="docutils literal notranslate"><span class="pre">feature_names</span></code> and <code class="docutils literal notranslate"><span class="pre">target_names</span></code>. See the dataset
descriptions below for details.</p>
<p><strong>The dataset generation functions.</strong> They can be used to generate controlled
synthetic datasets, described in the <a class="reference internal" href="datasets/sample_generators.html#sample-generators"><span class="std std-ref">Generated datasets</span></a> section.</p>
<p>These functions return a tuple <code class="docutils literal notranslate"><span class="pre">(X,</span> <span class="pre">y)</span></code> consisting of a <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> *
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> numpy array <code class="docutils literal notranslate"><span class="pre">X</span></code> and an array of length <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>
containing the targets <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
<p>In addition, there are also miscellaneous tools to load datasets of other
formats or from other locations, described in the <a class="reference internal" href="datasets/loading_other_datasets.html#loading-other-datasets"><span class="std std-ref">Loading other datasets</span></a>
section.</p>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#boston-house-prices-dataset">7.1.1. Boston house prices dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#iris-plants-dataset">7.1.2. Iris plants dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#diabetes-dataset">7.1.3. Diabetes dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset">7.1.4. Optical recognition of handwritten digits dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#linnerrud-dataset">7.1.5. Linnerrud dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#wine-recognition-dataset">7.1.6. Wine recognition dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#breast-cancer-wisconsin-diagnostic-dataset">7.1.7. Breast cancer wisconsin (diagnostic) dataset</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#the-olivetti-faces-dataset">7.2.1. The Olivetti faces dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#the-20-newsgroups-text-dataset">7.2.2. The 20 newsgroups text dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#the-labeled-faces-in-the-wild-face-recognition-dataset">7.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#forest-covertypes">7.2.4. Forest covertypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#rcv1-dataset">7.2.5. RCV1 dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#kddcup-99-dataset">7.2.6. Kddcup 99 dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#california-housing-dataset">7.2.7. California Housing dataset</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-classification-and-clustering">7.3.1. Generators for classification and clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-regression">7.3.2. Generators for regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-manifold-learning">7.3.3. Generators for manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-decomposition">7.3.4. Generators for decomposition</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#sample-images">7.4.1. Sample images</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#datasets-in-svmlight-libsvm-format">7.4.2. Datasets in svmlight / libsvm format</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#downloading-datasets-from-the-openml-org-repository">7.4.3. Downloading datasets from the openml.org repository</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#loading-from-external-datasets">7.4.4. Loading from external datasets</a></li>
</ul>
</li>
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