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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.fetch_20newsgroups_vectorized</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-20newsgroups-vectorized">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_20newsgroups_vectorized</span></code></a></li>
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<div class="section" id="sklearn-datasets-fetch-20newsgroups-vectorized">
<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>.fetch_20newsgroups_vectorized<a class="headerlink" href="#sklearn-datasets-fetch-20newsgroups-vectorized" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.fetch_20newsgroups_vectorized">
<code class="descclassname">sklearn.datasets.</code><code class="descname">fetch_20newsgroups_vectorized</code><span class="sig-paren">(</span><em>subset='train'</em>, <em>remove=()</em>, <em>data_home=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/datasets/twenty_newsgroups.py#L285"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups_vectorized" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the 20 newsgroups dataset and transform it into tf-idf vectors.</p>
<p>This is a convenience function; the tf-idf transformation is done using the
default settings for <cite>sklearn.feature_extraction.text.Vectorizer</cite>. For more
advanced usage (stopword filtering, n-gram extraction, etc.), combine
fetch_20newsgroups with a custom <cite>Vectorizer</cite> or <cite>CountVectorizer</cite>.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/twenty_newsgroups.html#newsgroups"><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>subset: ‘train’ or ‘test’, ‘all’, optional</strong> :</p>
<blockquote>
<div><p>Select the dataset to load: ‘train’ for the training set, ‘test’
for the test set, ‘all’ for both, with shuffled ordering.</p>
</div></blockquote>
<p><strong>data_home: optional, default: None</strong> :</p>
<blockquote>
<div><p>Specify an download and cache folder for the datasets. If None,
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
</div></blockquote>
<p><strong>remove: tuple</strong> :</p>
<blockquote>
<div><p>May contain any subset of (‘headers’, ‘footers’, ‘quotes’). Each of
these are kinds of text that will be detected and removed from the
newsgroup posts, preventing classifiers from overfitting on
metadata.</p>
<p>‘headers’ removes newsgroup headers, ‘footers’ removes blocks at the
ends of posts that look like signatures, and ‘quotes’ removes lines
that appear to be quoting another post.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>bunch</strong> : Bunch object</p>
<blockquote class="last">
<div><p>bunch.data: sparse matrix, shape [n_samples, n_features]
bunch.target: array, shape [n_samples]
bunch.target_names: list, length [n_classes]</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-fetch-20newsgroups-vectorized">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_20newsgroups_vectorized</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-20newsgroups-vectorized" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly pr..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/./plot_johnson_lindenstrauss_bound.html"><img alt="../../_images/plot_johnson_lindenstrauss_bound1.png" src="../../_images/plot_johnson_lindenstrauss_bound1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_johnson_lindenstrauss_bound.html#example-plot-johnson-lindenstrauss-bound-py"><span>The Johnson-Lindenstrauss bound for embedding with random projections</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performa..."><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/applications/plot_model_complexity_influence.html"><img alt="../../_images/plot_model_complexity_influence1.png" src="../../_images/plot_model_complexity_influence1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/plot_model_complexity_influence.html#example-applications-plot-model-complexity-influence-py"><span>Model Complexity Influence</span></a></span></p>
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