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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><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cross-validation-train-test-split">Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.train_test_split</span></code></a></li>
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<div class="section" id="sklearn-cross-validation-train-test-split">
<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>.train_test_split<a class="headerlink" href="#sklearn-cross-validation-train-test-split" title="Permalink to this headline">¶</a></h1>
<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/51a765a/sklearn/cross_validation.py#L1792"><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>
<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>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 class="versionadded">
<p><span class="versionmodified">New in version 0.16: </span>preserves input type instead of always casting to numpy array.</p>
</div>
</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 or RandomState</p>
<blockquote>
<div><p>Pseudo-random number generator state used for random sampling.</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>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-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">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="kn">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-python"><div class="highlight"><pre><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>
</dd></dl>
<div class="section" id="examples-using-sklearn-cross-validation-train-test-split">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cross_validation.train_test_split</span></code><a class="headerlink" href="#examples-using-sklearn-cross-validation-train-test-split" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", ..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/applications/face_recognition.html"><img alt="../../_images/face_recognition1.png" src="../../_images/face_recognition1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/face_recognition.html#example-applications-face-recognition-py"><span>Faces recognition example using eigenfaces and SVMs</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="When performing classification one often wants to predict not only the class label, but also th..."><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/calibration/plot_calibration_curve.html"><img alt="../../_images/plot_calibration_curve1.png" src="../../_images/plot_calibration_curve1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/calibration/plot_calibration_curve.html#example-calibration-plot-calibration-curve-py"><span>Probability Calibration curves</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="When performing classification you often want to predict not only the class label, but also the..."><div class="figure" id="id3">
<a class="reference external image-reference" href="./../../auto_examples/calibration/plot_calibration.html"><img alt="../../_images/plot_calibration1.png" src="../../_images/plot_calibration1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/calibration/plot_calibration.html#example-calibration-plot-calibration-py"><span>Probability calibration of classifiers</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this ..."><div class="figure" id="id4">
<a class="reference external image-reference" href="./../../auto_examples/classification/plot_classifier_comparison.html"><img alt="../../_images/plot_classifier_comparison1.png" src="../../_images/plot_classifier_comparison1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py"><span>Classifier comparison</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Partial dependence plots show the dependence between the target function [2]_ and a set of 'tar..."><div class="figure" id="id5">
<a class="reference external image-reference" href="./../../auto_examples/ensemble/plot_partial_dependence.html"><img alt="../../_images/plot_partial_dependence1.png" src="../../_images/plot_partial_dependence1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_partial_dependence.html#example-ensemble-plot-partial-dependence-py"><span>Partial Dependence Plots</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on t..."><div class="figure" id="id6">
<a class="reference external image-reference" href="./../../auto_examples/ensemble/plot_feature_transformation.html"><img alt="../../_images/plot_feature_transformation1.png" src="../../_images/plot_feature_transformation1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_feature_transformation.html#example-ensemble-plot-feature-transformation-py"><span>Feature transformations with ensembles of trees</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Out-of-bag (OOB) estimates can be a useful heuristic to estimate the "optimal" number of boosti..."><div class="figure" id="id7">
<a class="reference external image-reference" href="./../../auto_examples/ensemble/plot_gradient_boosting_oob.html"><img alt="../../_images/plot_gradient_boosting_oob1.png" src="../../_images/plot_gradient_boosting_oob1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_oob.html#example-ensemble-plot-gradient-boosting-oob-py"><span>Gradient Boosting Out-of-Bag estimates</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="An example showing how different online solvers perform on the hand-written digits dataset."><div class="figure" id="id8">
<a class="reference external image-reference" href="./../../auto_examples/linear_model/plot_sgd_comparison.html"><img alt="../../_images/plot_sgd_comparison1.png" src="../../_images/plot_sgd_comparison1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_sgd_comparison.html#example-linear-model-plot-sgd-comparison-py"><span>Comparing various online solvers</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Example of confusion matrix usage to evaluate the quality of the output of a classifier on the ..."><div class="figure" id="id9">
<a class="reference external image-reference" href="./../../auto_examples/model_selection/plot_confusion_matrix.html"><img alt="../../_images/plot_confusion_matrix1.png" src="../../_images/plot_confusion_matrix1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_confusion_matrix.html#example-model-selection-plot-confusion-matrix-py"><span>Confusion matrix</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the ..."><div class="figure" id="id10">
<a class="reference external image-reference" href="./../../auto_examples/model_selection/grid_search_digits.html"><img alt="../../_images/grid_search_digits1.png" src="../../_images/grid_search_digits1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/grid_search_digits.html#example-model-selection-grid-search-digits-py"><span>Parameter estimation using grid search with cross-validation</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Example of Precision-Recall metric to evaluate classifier output quality."><div class="figure" id="id11">
<a class="reference external image-reference" href="./../../auto_examples/model_selection/plot_precision_recall.html"><img alt="../../_images/plot_precision_recall1.png" src="../../_images/plot_precision_recall1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_precision_recall.html#example-model-selection-plot-precision-recall-py"><span>Precision-Recall</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality..."><div class="figure" id="id12">
<a class="reference external image-reference" href="./../../auto_examples/model_selection/plot_roc.html"><img alt="../../_images/plot_roc1.png" src="../../_images/plot_roc1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_roc.html#example-model-selection-plot-roc-py"><span>Receiver Operating Characteristic (ROC)</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="For greyscale image data where pixel values can be interpreted as degrees of blackness on a whi..."><div class="figure" id="id13">
<a class="reference external image-reference" href="./../../auto_examples/neural_networks/plot_rbm_logistic_classification.html"><img alt="../../_images/plot_rbm_logistic_classification1.png" src="../../_images/plot_rbm_logistic_classification1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neural_networks/plot_rbm_logistic_classification.html#example-neural-networks-plot-rbm-logistic-classification-py"><span>Restricted Boltzmann Machine features for digit classification</span></a></span></p>
</div>
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<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/preprocessing/plot_function_transformer.html#example-preprocessing-plot-function-transformer-py"><span>Using FunctionTransformer to select columns</span></a></span></p>
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