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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.train_test_split</a></li>
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<div class="section" id="sklearn-cross-validation-train-test-split">
<h1><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_validation</span></code>.train_test_split<a class="headerlink" href="#sklearn-cross-validation-train-test-split" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last"><strong>DEPRECATED</strong></p>
</div>
<dl class="function">
<dt id="sklearn.cross_validation.train_test_split">
<code class="descclassname">sklearn.cross_validation.</code><code class="descname">train_test_split</code><span class="sig-paren">(</span><em>*arrays</em>, <em>**options</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/cross_validation.py#L1956"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.train_test_split" title="Permalink to this definition">¶</a></dt>
<dd><p>Split arrays or matrices into random train and test subsets</p>
<div class="deprecated">
<p><span class="versionmodified">Deprecated since version 0.18: </span>This module will be removed in 0.20.
Use <a class="reference internal" href="sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split"><code class="xref py py-func docutils literal"><span class="pre">sklearn.model_selection.train_test_split</span></code></a> instead.</p>
</div>
<p>Quick utility that wraps input validation and
<code class="docutils literal"><span class="pre">next(iter(ShuffleSplit(n_samples)))</span></code> and application to input
data into a single call for splitting (and optionally subsampling)
data in a oneliner.</p>
<p>Read more in the <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>*arrays</strong> : sequence of indexables with same length / shape[0]</p>
<blockquote>
<div><p>Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.</p>
</div></blockquote>
<p><strong>test_size</strong> : float, int, or None (default is None)</p>
<blockquote>
<div><p>If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the test split. If
int, represents the absolute number of test samples. If None,
the value is automatically set to the complement of the train size.
If train size is also None, test size is set to 0.25.</p>
</div></blockquote>
<p><strong>train_size</strong> : float, int, or None (default is None)</p>
<blockquote>
<div><p>If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.</p>
</div></blockquote>
<p><strong>random_state</strong> : int, RandomState instance or None, optional (default=None)</p>
<blockquote>
<div><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <cite>np.random</cite>.</p>
</div></blockquote>
<p><strong>stratify</strong> : array-like or None (default is None)</p>
<blockquote>
<div><p>If not None, data is split in a stratified fashion, using this as
the labels array.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.17: </span><em>stratify</em> splitting</p>
</div>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>splitting</strong> : list, length = 2 * len(arrays),</p>
<blockquote class="last">
<div><p>List containing train-test split of inputs.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.16: </span>If the input is sparse, the output will be a
<code class="docutils literal"><span class="pre">scipy.sparse.csr_matrix</span></code>. Else, output type is the same as the
input type.</p>
</div>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span>
<span class="go">array([[0, 1],</span>
<span class="go"> [2, 3],</span>
<span class="go"> [4, 5],</span>
<span class="go"> [6, 7],</span>
<span class="go"> [8, 9]])</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="go">[0, 1, 2, 3, 4]</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">X_train</span>
<span class="go">array([[4, 5],</span>
<span class="go"> [0, 1],</span>
<span class="go"> [6, 7]])</span>
<span class="gp">>>> </span><span class="n">y_train</span>
<span class="go">[2, 0, 3]</span>
<span class="gp">>>> </span><span class="n">X_test</span>
<span class="go">array([[2, 3],</span>
<span class="go"> [8, 9]])</span>
<span class="gp">>>> </span><span class="n">y_test</span>
<span class="go">[1, 4]</span>
</pre></div>
</div>
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