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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.base</span></code>.TransformerMixin</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-base-transformermixin">Examples using <code class="docutils literal"><span class="pre">sklearn.base.TransformerMixin</span></code></a></li>
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<h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></code></a>.TransformerMixin<a class="headerlink" href="#sklearn-base-transformermixin" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.base.TransformerMixin">
<em class="property">class </em><code class="descclassname">sklearn.base.</code><code class="descname">TransformerMixin</code><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py#L428"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Permalink to this definition">¶</a></dt>
<dd><p>Mixin class for all transformers in scikit-learn.</p>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.base.TransformerMixin.fit_transform" title="sklearn.base.TransformerMixin.fit_transform"><code class="xref py py-obj docutils literal"><span class="pre">fit_transform</span></code></a>(X[, y])</td>
<td>Fit to data, then transform it.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.base.TransformerMixin.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#sklearn.base.TransformerMixin.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self. See help(type(self)) for accurate signature.</p>
</dd></dl>
<dl class="method">
<dt id="sklearn.base.TransformerMixin.fit_transform">
<code class="descname">fit_transform</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py#L431"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : numpy array of shape [n_samples, n_features]</p>
<blockquote>
<div><p>Training set.</p>
</div></blockquote>
<p><strong>y</strong> : numpy array of shape [n_samples]</p>
<blockquote>
<div><p>Target values.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X_new</strong> : numpy array of shape [n_samples, n_features_new]</p>
<blockquote class="last">
<div><p>Transformed array.</p>
</div></blockquote>
</td>
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<div class="section" id="examples-using-sklearn-base-transformermixin">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.base.TransformerMixin</span></code><a class="headerlink" href="#examples-using-sklearn-base-transformermixin" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="Datasets can often contain components of that require different feature extraction and processi..."><div class="figure" id="id1">
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<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/hetero_feature_union.html#example-hetero-feature-union-py"><span>Feature Union with Heterogeneous Data Sources</span></a></span></p>
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