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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code>.ClassifierMixin</a></li>
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<section id="sklearn-base-classifiermixin">
<h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code></a>.ClassifierMixin<a class="headerlink" href="#sklearn-base-classifiermixin" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.base.ClassifierMixin">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">ClassifierMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/6cb2c5237/sklearn/base.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassifierMixin" title="Permalink to this definition">¶</a></dt>
<dd><p>Mixin class for all classifiers in scikit-learn.</p>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.base.ClassifierMixin.score" title="sklearn.base.ClassifierMixin.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
<td><p>Return the mean accuracy on the given test data and labels.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.base.ClassifierMixin.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/6cb2c5237/sklearn/base.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.ClassifierMixin.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return 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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> wrt. <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
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