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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.make_multilabel_classification</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-multilabel-classification">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.make_multilabel_classification</span></code></a></li>
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<div class="section" id="sklearn-datasets-make-multilabel-classification">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>.make_multilabel_classification<a class="headerlink" href="#sklearn-datasets-make-multilabel-classification" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.make_multilabel_classification">
<code class="descclassname">sklearn.datasets.</code><code class="descname">make_multilabel_classification</code><span class="sig-paren">(</span><em>n_samples=100</em>, <em>n_features=20</em>, <em>n_classes=5</em>, <em>n_labels=2</em>, <em>length=50</em>, <em>allow_unlabeled=True</em>, <em>sparse=False</em>, <em>return_indicator='dense'</em>, <em>return_distributions=False</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/datasets/samples_generator.py#L250"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_multilabel_classification" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate a random multilabel classification problem.</p>
<dl class="docutils">
<dt>For each sample, the generative process is:</dt>
<dd><ul class="first last simple">
<li>pick the number of labels: n ~ Poisson(n_labels)</li>
<li>n times, choose a class c: c ~ Multinomial(theta)</li>
<li>pick the document length: k ~ Poisson(length)</li>
<li>k times, choose a word: w ~ Multinomial(theta_c)</li>
</ul>
</dd>
</dl>
<p>In the above process, rejection sampling is used to make sure that
n is never zero or more than <cite>n_classes</cite>, and that the document length
is never zero. Likewise, we reject classes which have already been chosen.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#sample-generators"><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_samples</strong> : int, optional (default=100)</p>
<blockquote>
<div><p>The number of samples.</p>
</div></blockquote>
<p><strong>n_features</strong> : int, optional (default=20)</p>
<blockquote>
<div><p>The total number of features.</p>
</div></blockquote>
<p><strong>n_classes</strong> : int, optional (default=5)</p>
<blockquote>
<div><p>The number of classes of the classification problem.</p>
</div></blockquote>
<p><strong>n_labels</strong> : int, optional (default=2)</p>
<blockquote>
<div><p>The average number of labels per instance. More precisely, the number
of labels per sample is drawn from a Poisson distribution with
<code class="docutils literal"><span class="pre">n_labels</span></code> as its expected value, but samples are bounded (using
rejection sampling) by <code class="docutils literal"><span class="pre">n_classes</span></code>, and must be nonzero if
<code class="docutils literal"><span class="pre">allow_unlabeled</span></code> is False.</p>
</div></blockquote>
<p><strong>length</strong> : int, optional (default=50)</p>
<blockquote>
<div><p>The sum of the features (number of words if documents) is drawn from
a Poisson distribution with this expected value.</p>
</div></blockquote>
<p><strong>allow_unlabeled</strong> : bool, optional (default=True)</p>
<blockquote>
<div><p>If <code class="docutils literal"><span class="pre">True</span></code>, some instances might not belong to any class.</p>
</div></blockquote>
<p><strong>sparse</strong> : bool, optional (default=False)</p>
<blockquote>
<div><p>If <code class="docutils literal"><span class="pre">True</span></code>, return a sparse feature matrix</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.17: </span>parameter to allow <em>sparse</em> output.</p>
</div>
</div></blockquote>
<p><strong>return_indicator</strong> : ‘dense’ (default) | ‘sparse’ | False</p>
<blockquote>
<div><p>If <code class="docutils literal"><span class="pre">dense</span></code> return <code class="docutils literal"><span class="pre">Y</span></code> in the dense binary indicator format. If
<code class="docutils literal"><span class="pre">'sparse'</span></code> return <code class="docutils literal"><span class="pre">Y</span></code> in the sparse binary indicator format.
<code class="docutils literal"><span class="pre">False</span></code> returns a list of lists of labels.</p>
</div></blockquote>
<p><strong>return_distributions</strong> : bool, optional (default=False)</p>
<blockquote>
<div><p>If <code class="docutils literal"><span class="pre">True</span></code>, return the prior class probability and conditional
probabilities of features given classes, from which the data was
drawn.</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>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X</strong> : array of shape [n_samples, n_features]</p>
<blockquote>
<div><p>The generated samples.</p>
</div></blockquote>
<p><strong>Y</strong> : array or sparse CSR matrix of shape [n_samples, n_classes]</p>
<blockquote>
<div><p>The label sets.</p>
</div></blockquote>
<p><strong>p_c</strong> : array, shape [n_classes]</p>
<blockquote>
<div><p>The probability of each class being drawn. Only returned if
<code class="docutils literal"><span class="pre">return_distributions=True</span></code>.</p>
</div></blockquote>
<p><strong>p_w_c</strong> : array, shape [n_features, n_classes]</p>
<blockquote class="last">
<div><p>The probability of each feature being drawn given each class.
Only returned if <code class="docutils literal"><span class="pre">return_distributions=True</span></code>.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-make-multilabel-classification">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.make_multilabel_classification</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-multilabel-classification" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example simulates a multi-label document classification problem. The dataset is generated ..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/./plot_multilabel.html"><img alt="../../_images/plot_multilabel1.png" src="../../_images/plot_multilabel1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_multilabel.html#example-plot-multilabel-py"><span>Multilabel classification</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This illustrates the `datasets.make_multilabel_classification` dataset generator. Each sample c..."><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/datasets/plot_random_multilabel_dataset.html"><img alt="../../_images/plot_random_multilabel_dataset1.png" src="../../_images/plot_random_multilabel_dataset1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/datasets/plot_random_multilabel_dataset.html#example-datasets-plot-random-multilabel-dataset-py"><span>Plot randomly generated multilabel dataset</span></a></span></p>
</div>
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