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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.check_cv</a></li>
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<div class="section" id="sklearn-cross-validation-check-cv">
<h1><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_validation</span></code>.check_cv<a class="headerlink" href="#sklearn-cross-validation-check-cv" 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.check_cv">
<code class="descclassname">sklearn.cross_validation.</code><code class="descname">check_cv</code><span class="sig-paren">(</span><em>cv</em>, <em>X=None</em>, <em>y=None</em>, <em>classifier=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/cross_validation.py#L1788"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.check_cv" title="Permalink to this definition">¶</a></dt>
<dd><p>Input checker utility for building a CV in a user friendly way.</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.check_cv.html#sklearn.model_selection.check_cv" title="sklearn.model_selection.check_cv"><code class="xref py py-func docutils literal"><span class="pre">sklearn.model_selection.check_cv</span></code></a> instead.</p>
</div>
<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>cv</strong> : int, cross-validation generator or an iterable, optional</p>
<blockquote>
<div><p>Determines the cross-validation splitting strategy.
Possible inputs for cv are:</p>
<ul class="simple">
<li>None, to use the default 3-fold cross-validation,</li>
<li>integer, to specify the number of folds.</li>
<li>An object to be used as a cross-validation generator.</li>
<li>An iterable yielding train/test splits.</li>
</ul>
<p>For integer/None inputs, if classifier is True and <code class="docutils literal"><span class="pre">y</span></code> is binary or
multiclass, <a class="reference internal" href="sklearn.cross_validation.StratifiedKFold.html#sklearn.cross_validation.StratifiedKFold" title="sklearn.cross_validation.StratifiedKFold"><code class="xref py py-class docutils literal"><span class="pre">StratifiedKFold</span></code></a> is used. In all other cases,
<a class="reference internal" href="sklearn.cross_validation.KFold.html#sklearn.cross_validation.KFold" title="sklearn.cross_validation.KFold"><code class="xref py py-class docutils literal"><span class="pre">KFold</span></code></a> is used.</p>
<p>Refer <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a> for the various
cross-validation strategies that can be used here.</p>
</div></blockquote>
<p><strong>X</strong> : array-like</p>
<blockquote>
<div><p>The data the cross-val object will be applied on.</p>
</div></blockquote>
<p><strong>y</strong> : array-like</p>
<blockquote>
<div><p>The target variable for a supervised learning problem.</p>
</div></blockquote>
<p><strong>classifier</strong> : boolean optional</p>
<blockquote>
<div><p>Whether the task is a classification task, in which case
stratified KFold will be used.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>checked_cv</strong> : a cross-validation generator instance.</p>
<blockquote class="last">
<div><p>The return value is guaranteed to be a cv generator instance, whatever
the input type.</p>
</div></blockquote>
</td>
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