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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.StratifiedShuffleSplit</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cross-validation-stratifiedshufflesplit">Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.StratifiedShuffleSplit</span></code></a></li>
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<div class="section" id="sklearn-cross-validation-stratifiedshufflesplit">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cross_validation" title="sklearn.cross_validation"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_validation</span></code></a>.StratifiedShuffleSplit<a class="headerlink" href="#sklearn-cross-validation-stratifiedshufflesplit" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_validation.StratifiedShuffleSplit">
<em class="property">class </em><code class="descclassname">sklearn.cross_validation.</code><code class="descname">StratifiedShuffleSplit</code><span class="sig-paren">(</span><em>y</em>, <em>n_iter=10</em>, <em>test_size=0.1</em>, <em>train_size=None</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cross_validation.py#L894"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.StratifiedShuffleSplit" title="Permalink to this definition">¶</a></dt>
<dd><p>Stratified ShuffleSplit cross validation iterator</p>
<p>Provides train/test indices to split data in train test sets.</p>
<p>This cross-validation object is a merge of StratifiedKFold and
ShuffleSplit, which returns stratified randomized folds. The folds
are made by preserving the percentage of samples for each class.</p>
<p>Note: like the ShuffleSplit strategy, stratified random splits
do not guarantee that all folds will be different, although this is
still very likely for sizeable datasets.</p>
<p>Read more in the <a class="reference internal" href="../cross_validation.html#cross-validation"><span>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>y</strong> : array, [n_samples]</p>
<blockquote>
<div><p>Labels of samples.</p>
</div></blockquote>
<p><strong>n_iter</strong> : int (default 10)</p>
<blockquote>
<div><p>Number of re-shuffling & splitting iterations.</p>
</div></blockquote>
<p><strong>test_size</strong> : float (default 0.1), int, or 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.</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 or RandomState</p>
<blockquote class="last">
<div><p>Pseudo-random number generator state used for random sampling.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">StratifiedShuffleSplit</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">sss</span> <span class="o">=</span> <span class="n">StratifiedShuffleSplit</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">sss</span><span class="p">)</span>
<span class="go">3</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">sss</span><span class="p">)</span>
<span class="go">StratifiedShuffleSplit(labels=[0 0 1 1], n_iter=3, ...)</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">train_index</span><span class="p">,</span> <span class="n">test_index</span> <span class="ow">in</span> <span class="n">sss</span><span class="p">:</span>
<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="s2">"TRAIN:"</span><span class="p">,</span> <span class="n">train_index</span><span class="p">,</span> <span class="s2">"TEST:"</span><span class="p">,</span> <span class="n">test_index</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="gp">... </span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="go">TRAIN: [1 2] TEST: [3 0]</span>
<span class="go">TRAIN: [0 2] TEST: [1 3]</span>
<span class="go">TRAIN: [0 2] TEST: [3 1]</span>
<span class="go">.. automethod:: __init__</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="examples-using-sklearn-cross-validation-stratifiedshufflesplit">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.StratifiedShuffleSplit</span></code><a class="headerlink" href="#examples-using-sklearn-cross-validation-stratifiedshufflesplit" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example illustrates the effect of the parameters ``gamma`` and ``C`` of the Radial Basis F..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/svm/plot_rbf_parameters.html"><img alt="../../_images/plot_rbf_parameters1.png" src="../../_images/plot_rbf_parameters1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/svm/plot_rbf_parameters.html#example-svm-plot-rbf-parameters-py"><span>RBF SVM parameters</span></a></span></p>
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