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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.fetch_20newsgroups</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-20newsgroups">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_20newsgroups</span></code></a></li>
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<div class="section" id="sklearn-datasets-fetch-20newsgroups">
<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<a class="headerlink" href="#sklearn-datasets-fetch-20newsgroups" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.fetch_20newsgroups">
<code class="descclassname">sklearn.datasets.</code><code class="descname">fetch_20newsgroups</code><span class="sig-paren">(</span><em>data_home=None</em>, <em>subset='train'</em>, <em>categories=None</em>, <em>shuffle=True</em>, <em>random_state=42</em>, <em>remove=()</em>, <em>download_if_missing=True</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#L154"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the filenames and data from the 20 newsgroups dataset.</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 a 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>categories: None or collection of string or unicode</strong> :</p>
<blockquote>
<div><p>If None (default), load all the categories.
If not None, list of category names to load (other categories
ignored).</p>
</div></blockquote>
<p><strong>shuffle: bool, optional</strong> :</p>
<blockquote>
<div><p>Whether or not to shuffle the data: might be important for models that
make the assumption that the samples are independent and identically
distributed (i.i.d.), such as stochastic gradient descent.</p>
</div></blockquote>
<p><strong>random_state: numpy random number generator or seed integer</strong> :</p>
<blockquote>
<div><p>Used to shuffle the dataset.</p>
</div></blockquote>
<p><strong>download_if_missing: optional, True by default</strong> :</p>
<blockquote>
<div><p>If False, raise an IOError if the data is not locally available
instead of trying to download the data from the source site.</p>
</div></blockquote>
<p><strong>remove: tuple</strong> :</p>
<blockquote class="last">
<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>
<p>‘headers’ follows an exact standard; the other filters are not always
correct.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-fetch-20newsgroups">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_20newsgroups</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-20newsgroups" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="Datasets can often contain components of that require different feature extraction and processi..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/./hetero_feature_union.html"><img alt="../../_images/hetero_feature_union1.png" src="../../_images/hetero_feature_union1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/hetero_feature_union.html#example-hetero-feature-union-py"><span>Feature Union with Heterogeneous Data Sources</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocatio..."><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/applications/topics_extraction_with_nmf_lda.html"><img alt="../../_images/topics_extraction_with_nmf_lda1.png" src="../../_images/topics_extraction_with_nmf_lda1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/topics_extraction_with_nmf_lda.html#example-applications-topics-extraction-with-nmf-lda-py"><span>Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset..."><div class="figure" id="id3">
<a class="reference external image-reference" href="./../../auto_examples/bicluster/bicluster_newsgroups.html"><img alt="../../_images/bicluster_newsgroups1.png" src="../../_images/bicluster_newsgroups1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/bicluster/bicluster_newsgroups.html#example-bicluster-bicluster-newsgroups-py"><span>Biclustering documents with the Spectral Co-clustering algorithm</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="The dataset used in this example is the 20 newsgroups dataset which will be automatically downl..."><div class="figure" id="id4">
<a class="reference external image-reference" href="./../../auto_examples/model_selection/grid_search_text_feature_extraction.html"><img alt="../../_images/grid_search_text_feature_extraction1.png" src="../../_images/grid_search_text_feature_extraction1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/grid_search_text_feature_extraction.html#example-model-selection-grid-search-text-feature-extraction-py"><span>Sample pipeline for text feature extraction and evaluation</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Compares FeatureHasher and DictVectorizer by using both to vectorize text documents."><div class="figure" id="id5">
<a class="reference external image-reference" href="./../../auto_examples/text/hashing_vs_dict_vectorizer.html"><img alt="../../_images/hashing_vs_dict_vectorizer1.png" src="../../_images/hashing_vs_dict_vectorizer1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/text/hashing_vs_dict_vectorizer.html#example-text-hashing-vs-dict-vectorizer-py"><span>FeatureHasher and DictVectorizer Comparison</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This is an example showing how the scikit-learn can be used to cluster documents by topics usin..."><div class="figure" id="id6">
<a class="reference external image-reference" href="./../../auto_examples/text/document_clustering.html"><img alt="../../_images/document_clustering1.png" src="../../_images/document_clustering1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/text/document_clustering.html#example-text-document-clustering-py"><span>Clustering text documents using k-means</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This is an example showing how scikit-learn can be used to classify documents by topics using a..."><div class="figure" id="id7">
<a class="reference external image-reference" href="./../../auto_examples/text/document_classification_20newsgroups.html"><img alt="../../_images/document_classification_20newsgroups1.png" src="../../_images/document_classification_20newsgroups1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/text/document_classification_20newsgroups.html#example-text-document-classification-20newsgroups-py"><span>Classification of text documents using sparse features</span></a></span></p>
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