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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.dump_svmlight_file</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-dump-svmlight-file">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.dump_svmlight_file</span></code></a></li>
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  <div class="section" id="sklearn-datasets-dump-svmlight-file">
<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>.dump_svmlight_file<a class="headerlink" href="#sklearn-datasets-dump-svmlight-file" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.dump_svmlight_file">
<code class="descclassname">sklearn.datasets.</code><code class="descname">dump_svmlight_file</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>f</em>, <em>zero_based=True</em>, <em>comment=None</em>, <em>query_id=None</em>, <em>multilabel=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/datasets/svmlight_format.py#L376"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.dump_svmlight_file" title="Permalink to this definition">¶</a></dt>
<dd><p>Dump the dataset in svmlight / libsvm file format.</p>
<p>This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.</p>
<p>The first element of each line can be used to store a target variable
to predict.</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>X</strong> : {array-like, sparse matrix}, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training vectors, where n_samples is the number of samples and
n_features is the number of features.</p>
</div></blockquote>
<p><strong>y</strong> : {array-like, sparse matrix}, shape = [n_samples (, n_labels)]</p>
<blockquote>
<div><p>Target values. Class labels must be an
integer or float, or array-like objects of integer or float for
multilabel classifications.</p>
</div></blockquote>
<p><strong>f</strong> : string or file-like in binary mode</p>
<blockquote>
<div><p>If string, specifies the path that will contain the data.
If file-like, data will be written to f. f should be opened in binary
mode.</p>
</div></blockquote>
<p><strong>zero_based</strong> : boolean, optional</p>
<blockquote>
<div><p>Whether column indices should be written zero-based (True) or one-based
(False).</p>
</div></blockquote>
<p><strong>comment</strong> : string, optional</p>
<blockquote>
<div><p>Comment to insert at the top of the file. This should be either a
Unicode string, which will be encoded as UTF-8, or an ASCII byte
string.
If a comment is given, then it will be preceded by one that identifies
the file as having been dumped by scikit-learn. Note that not all
tools grok comments in SVMlight files.</p>
</div></blockquote>
<p><strong>query_id</strong> : array-like, shape = [n_samples]</p>
<blockquote>
<div><p>Array containing pairwise preference constraints (qid in svmlight
format).</p>
</div></blockquote>
<p><strong>multilabel</strong> : boolean, optional</p>
<blockquote class="last">
<div><p>Samples may have several labels each (see
<a class="reference external" href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html">http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html</a>)</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.17: </span>parameter <em>multilabel</em> to support multilabel datasets.</p>
</div>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<div class="section" id="examples-using-sklearn-datasets-dump-svmlight-file">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.dump_svmlight_file</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-dump-svmlight-file" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create da..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_svm_gui_thumb.png" src="../../_images/sphx_glr_svm_gui_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/svm_gui.html#sphx-glr-auto-examples-applications-svm-gui-py"><span class="std std-ref">Libsvm GUI</span></a></span></p>
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