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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.LeaveOneOut</a></li>
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<div class="section" id="sklearn-cross-validation-leaveoneout">
<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>.LeaveOneOut<a class="headerlink" href="#sklearn-cross-validation-leaveoneout" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_validation.LeaveOneOut">
<em class="property">class </em><code class="descclassname">sklearn.cross_validation.</code><code class="descname">LeaveOneOut</code><span class="sig-paren">(</span><em>n</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cross_validation.py#L97"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.LeaveOneOut" title="Permalink to this definition">¶</a></dt>
<dd><p>Leave-One-Out cross validation iterator.</p>
<p>Provides train/test indices to split data in train test sets. Each
sample is used once as a test set (singleton) while the remaining
samples form the training set.</p>
<p>Note: <code class="docutils literal"><span class="pre">LeaveOneOut(n)</span></code> is equivalent to <code class="docutils literal"><span class="pre">KFold(n,</span> <span class="pre">n_folds=n)</span></code> and
<code class="docutils literal"><span class="pre">LeavePOut(n,</span> <span class="pre">p=1)</span></code>.</p>
<p>Due to the high number of test sets (which is the same as the
number of samples) this cross validation method can be very costly.
For large datasets one should favor KFold, StratifiedKFold or
ShuffleSplit.</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>n</strong> : int</p>
<blockquote class="last">
<div><p>Total number of elements in dataset.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="sklearn.cross_validation.LeaveOneLabelOut.html#sklearn.cross_validation.LeaveOneLabelOut" title="sklearn.cross_validation.LeaveOneLabelOut"><code class="xref py py-obj docutils literal"><span class="pre">LeaveOneLabelOut</span></code></a>, <code class="xref py py-obj docutils literal"><span class="pre">domain-specific</span></code></p>
</div>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">cross_validation</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="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">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">loo</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">LeaveOneOut</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">loo</span><span class="p">)</span>
<span class="go">2</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">loo</span><span class="p">)</span>
<span class="go">sklearn.cross_validation.LeaveOneOut(n=2)</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">loo</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="gp">... </span> <span class="k">print</span><span class="p">(</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="p">)</span>
<span class="go">TRAIN: [1] TEST: [0]</span>
<span class="go">[[3 4]] [[1 2]] [2] [1]</span>
<span class="go">TRAIN: [0] TEST: [1]</span>
<span class="go">[[1 2]] [[3 4]] [1] [2]</span>
<span class="go">.. automethod:: __init__</span>
</pre></div>
</div>
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