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<li class="toctree-l1 has-children"><a class="reference internal" href="sklearn.mixture.html">sklearn.mixture</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l1 has-children"><a class="reference internal" href="sklearn.model_selection.html">sklearn.model_selection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">sklearn.multiclass</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l1 has-children"><a class="reference internal" href="sklearn.preprocessing.html">sklearn.preprocessing</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<section id="module-sklearn.multiclass">
<span id="sklearn-multiclass"></span><span id="multiclass-ref"></span><h1>sklearn.multiclass<a class="headerlink" href="#module-sklearn.multiclass" title="Link to this heading">#</a></h1>
<p>Multiclass learning algorithms.</p>
<ul class="simple">
<li><p>one-vs-the-rest / one-vs-all</p></li>
<li><p>one-vs-one</p></li>
<li><p>error correcting output codes</p></li>
</ul>
<p>The estimators provided in this module are meta-estimators: they require a base
estimator to be provided in their constructor. For example, it is possible to
use these estimators to turn a binary classifier or a regressor into a
multiclass classifier. It is also possible to use these estimators with
multiclass estimators in the hope that their accuracy or runtime performance
improves.</p>
<p>All classifiers in scikit-learn implement multiclass classification; you
only need to use this module if you want to experiment with custom multiclass
strategies.</p>
<p>The one-vs-the-rest meta-classifier also implements a <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method,
so long as such a method is implemented by the base classifier. This method
returns probabilities of class membership in both the single label and
multilabel case. Note that in the multilabel case, probabilities are the
marginal probability that a given sample falls in the given class. As such, in
the multilabel case the sum of these probabilities over all possible labels
for a given sample <em>will not</em> sum to unity, as they do in the single label
case.</p>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/multiclass.html#multiclass-classification"><span class="std std-ref">Multiclass classification</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneVsOneClassifier</span></code></a></p></td>
<td><p>One-vs-one multiclass strategy.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneVsRestClassifier</span></code></a></p></td>
<td><p>One-vs-the-rest (OvR) multiclass strategy.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.multiclass.OutputCodeClassifier.html#sklearn.multiclass.OutputCodeClassifier" title="sklearn.multiclass.OutputCodeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OutputCodeClassifier</span></code></a></p></td>
<td><p>(Error-Correcting) Output-Code multiclass strategy.</p></td>
</tr>
</tbody>
</table>
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