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  <section id="fetch-20newsgroups">
<h1>fetch_20newsgroups<a class="headerlink" href="#fetch-20newsgroups" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_20newsgroups">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_20newsgroups</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'train'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_retries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/datasets/_twenty_newsgroups.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups" title="Link to this definition">#</a></dt>
<dd><p>Load the filenames and data from the 20 newsgroups dataset (classification).</p>
<p>Download it if necessary.</p>
<div class="pst-scrollable-table-container"><table class="table">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>20</p></td>
</tr>
<tr class="row-even"><td><p>Samples total</p></td>
<td><p>18846</p></td>
</tr>
<tr class="row-odd"><td><p>Dimensionality</p></td>
<td><p>1</p></td>
</tr>
<tr class="row-even"><td><p>Features</p></td>
<td><p>text</p></td>
</tr>
</tbody>
</table>
</div>
<p>Read more in the <a class="reference internal" href="../../datasets/real_world.html#newsgroups-dataset"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>data_home</strong><span class="classifier">str or path-like, default=None</span></dt><dd><p>Specify a download and cache folder for the datasets. If None,
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
</dd>
<dt><strong>subset</strong><span class="classifier">{‘train’, ‘test’, ‘all’}, default=’train’</span></dt><dd><p>Select the dataset to load: ‘train’ for the training set, ‘test’
for the test set, ‘all’ for both, with shuffled ordering.</p>
</dd>
<dt><strong>categories</strong><span class="classifier">array-like, dtype=str, default=None</span></dt><dd><p>If None (default), load all the categories.
If not None, list of category names to load (other categories
ignored).</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><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>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=42</span></dt><dd><p>Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>remove</strong><span class="classifier">tuple, default=()</span></dt><dd><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>
</dd>
<dt><strong>download_if_missing</strong><span class="classifier">bool, default=True</span></dt><dd><p>If False, raise an OSError if the data is not locally available
instead of trying to download the data from the source site.</p>
</dd>
<dt><strong>return_X_y</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, returns <code class="docutils literal notranslate"><span class="pre">(data.data,</span> <span class="pre">data.target)</span></code> instead of a Bunch
object.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.22.</span></p>
</div>
</dd>
<dt><strong>n_retries</strong><span class="classifier">int, default=3</span></dt><dd><p>Number of retries when HTTP errors are encountered.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.5.</span></p>
</div>
</dd>
<dt><strong>delay</strong><span class="classifier">float, default=1.0</span></dt><dd><p>Number of seconds between retries.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.5.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>bunch</strong><span class="classifier"><a class="reference internal" href="sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Dictionary-like object, with the following attributes.</p>
<dl class="simple">
<dt>data<span class="classifier">list of shape (n_samples,)</span></dt><dd><p>The data list to learn.</p>
</dd>
<dt>target: ndarray of shape (n_samples,)</dt><dd><p>The target labels.</p>
</dd>
<dt>filenames: list of shape (n_samples,)</dt><dd><p>The path to the location of the data.</p>
</dd>
<dt>DESCR: str</dt><dd><p>The full description of the dataset.</p>
</dd>
<dt>target_names: list of shape (n_classes,)</dt><dd><p>The names of target classes.</p>
</dd>
</dl>
</dd>
<dt><strong>(data, target)</strong><span class="classifier">tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y=True</span></code></span></dt><dd><p>A tuple of two ndarrays. The first contains a 2D array of shape
(n_samples, n_classes) with each row representing one sample and each
column representing the features. The second array of shape
(n_samples,) contains the target samples.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_20newsgroups</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cats</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;alt.atheism&#39;</span><span class="p">,</span> <span class="s1">&#39;sci.space&#39;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">newsgroups_train</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</span><span class="n">cats</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="go">[&#39;alt.atheism&#39;, &#39;sci.space&#39;]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">filenames</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(1073,)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(1073,)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
<span class="go">array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])</span>
</pre></div>
</div>
</dd></dl>

<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This is an example of applying NMF and LatentDirichletAllocation on a corpus of documents and extract additive models of the topic structure of the corpus.  The output is a plot of topics, each represented as bar plot using top few words based on weights."><img alt="" src="../../_images/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py"><span class="std std-ref">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</span></a></p>
  <div class="sphx-glr-thumbnail-title">Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset. The &#x27;comp.os.ms-windows.misc&#x27; category is excluded because it contains many posts containing nothing but data."><img alt="" src="../../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">Biclustering documents with the Spectral Co-clustering algorithm</span></a></p>
  <div class="sphx-glr-thumbnail-title">Biclustering documents with the Spectral Co-clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Datasets can often contain components that require different feature extraction and processing pipelines. This scenario might occur when:"><img alt="" src="../../_images/sphx_glr_plot_column_transformer_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/compose/plot_column_transformer.html#sphx-glr-auto-examples-compose-plot-column-transformer-py"><span class="std std-ref">Column Transformer with Heterogeneous Data Sources</span></a></p>
  <div class="sphx-glr-thumbnail-title">Column Transformer with Heterogeneous Data Sources</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The dataset used in this example is 20newsgroups_dataset which will be automatically downloaded, cached and reused for the document classification example."><img alt="" src="../../_images/sphx_glr_plot_grid_search_text_feature_extraction_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-plot-grid-search-text-feature-extraction-py"><span class="std std-ref">Sample pipeline for text feature extraction and evaluation</span></a></p>
  <div class="sphx-glr-thumbnail-title">Sample pipeline for text feature extraction and evaluation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, semi-supervised classifiers are trained on the 20 newsgroups dataset (which will be automatically downloaded)."><img alt="" src="../../_images/sphx_glr_plot_semi_supervised_newsgroups_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/semi_supervised/plot_semi_supervised_newsgroups.html#sphx-glr-auto-examples-semi-supervised-plot-semi-supervised-newsgroups-py"><span class="std std-ref">Semi-supervised Classification on a Text Dataset</span></a></p>
  <div class="sphx-glr-thumbnail-title">Semi-supervised Classification on a Text Dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how scikit-learn can be used to classify documents by topics using a Bag of Words approach. This example uses a Tf-idf-weighted document-term sparse matrix to encode the features and demonstrates various classifiers that can efficiently handle sparse matrices."><img alt="" src="../../_images/sphx_glr_plot_document_classification_20newsgroups_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py"><span class="std std-ref">Classification of text documents using sparse features</span></a></p>
  <div class="sphx-glr-thumbnail-title">Classification of text documents using sparse features</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how the scikit-learn API can be used to cluster documents by topics using a Bag of Words approach."><img alt="" src="../../_images/sphx_glr_plot_document_clustering_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py"><span class="std std-ref">Clustering text documents using k-means</span></a></p>
  <div class="sphx-glr-thumbnail-title">Clustering text documents using k-means</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we illustrate text vectorization, which is the process of representing non-numerical input data (such as dictionaries or text documents) as vectors of real numbers."><img alt="" src="../../_images/sphx_glr_plot_hashing_vs_dict_vectorizer_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/text/plot_hashing_vs_dict_vectorizer.html#sphx-glr-auto-examples-text-plot-hashing-vs-dict-vectorizer-py"><span class="std std-ref">FeatureHasher and DictVectorizer Comparison</span></a></p>
  <div class="sphx-glr-thumbnail-title">FeatureHasher and DictVectorizer Comparison</div>
</div></div></section>
</section>


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