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              <ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.fetch_20newsgroups_vectorized</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.fetch_20newsgroups_vectorized"><code class="docutils literal notranslate"><span class="pre">fetch_20newsgroups_vectorized</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-20newsgroups-vectorized">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_20newsgroups_vectorized</span></code></a></li>
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  <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 notranslate"><span class="pre">sklearn.datasets</span></code></a>.fetch_20newsgroups_vectorized<a class="headerlink" href="#sklearn-datasets-fetch-20newsgroups-vectorized" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_20newsgroups_vectorized">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_20newsgroups_vectorized</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">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">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">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">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">normalize</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">as_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/datasets/_twenty_newsgroups.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_20newsgroups_vectorized" title="Permalink to this definition">¶</a></dt>
<dd><p>Load and vectorize the 20 newsgroups dataset (classification).</p>
<p>Download it if necessary.</p>
<p>This is a convenience function; the transformation is done using the
default settings for
<a class="reference internal" href="sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a>. For more
advanced usage (stopword filtering, n-gram extraction, etc.), combine
fetch_20newsgroups with a custom
<a class="reference internal" href="sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a>,
<a class="reference internal" href="sklearn.feature_extraction.text.HashingVectorizer.html#sklearn.feature_extraction.text.HashingVectorizer" title="sklearn.feature_extraction.text.HashingVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">HashingVectorizer</span></code></a>,
<a class="reference internal" href="sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer" title="sklearn.feature_extraction.text.TfidfTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">TfidfTransformer</span></code></a> or
<a class="reference internal" href="sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer" title="sklearn.feature_extraction.text.TfidfVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">TfidfVectorizer</span></code></a>.</p>
<p>The resulting counts are normalized using
<a class="reference internal" href="sklearn.preprocessing.normalize.html#sklearn.preprocessing.normalize" title="sklearn.preprocessing.normalize"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.preprocessing.normalize</span></code></a> unless normalize is set to False.</p>
<table class="docutils align-default">
<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>130107</p></td>
</tr>
<tr class="row-even"><td><p>Features</p></td>
<td><p>real</p></td>
</tr>
</tbody>
</table>
<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>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>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>
</dd>
<dt><strong>data_home</strong><span class="classifier">str, default=None</span></dt><dd><p>Specify an download and cache folder for the datasets. If None,
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</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">New in version 0.20.</span></p>
</div>
</dd>
<dt><strong>normalize</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, normalizes each document’s feature vector to unit norm using
<a class="reference internal" href="sklearn.preprocessing.normalize.html#sklearn.preprocessing.normalize" title="sklearn.preprocessing.normalize"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.preprocessing.normalize</span></code></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</dd>
<dt><strong>as_frame</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, the data is a pandas DataFrame including columns with
appropriate dtypes (numeric, string, or categorical). The target is
a pandas DataFrame or Series depending on the number of
<code class="docutils literal notranslate"><span class="pre">target_columns</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</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>
<dt>data: {sparse matrix, dataframe} of shape (n_samples, n_features)</dt><dd><p>The input data matrix. If <code class="docutils literal notranslate"><span class="pre">as_frame</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code> is
a pandas DataFrame with sparse columns.</p>
</dd>
<dt>target: {ndarray, series} of shape (n_samples,)</dt><dd><p>The target labels. If <code class="docutils literal notranslate"><span class="pre">as_frame</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, <code class="docutils literal notranslate"><span class="pre">target</span></code> is a
pandas Series.</p>
</dd>
<dt>target_names: list of shape (n_classes,)</dt><dd><p>The names of target classes.</p>
</dd>
<dt>DESCR: str</dt><dd><p>The full description of the dataset.</p>
</dd>
<dt>frame: dataframe of shape (n_samples, n_features + 1)</dt><dd><p>Only present when <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>. Pandas DataFrame with <code class="docutils literal notranslate"><span class="pre">data</span></code>
and <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
</dl>
</dd>
<dt><strong>(data, target)</strong><span class="classifier">tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code> would be of the format defined in the <code class="docutils literal notranslate"><span class="pre">Bunch</span></code>
description above.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<section id="examples-using-sklearn-datasets-fetch-20newsgroups-vectorized">
<h2>Examples using <code class="docutils literal notranslate"><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 heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performa..."><img alt="" src="../../_images/sphx_glr_plot_model_complexity_influence_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py"><span class="std std-ref">Model Complexity Influence</span></a></p>
  <div class="sphx-glr-thumbnail-title">Model Complexity Influence</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression to classify doc..."><img alt="" src="../../_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py"><span class="std std-ref">Multiclass sparse logistic regression on 20newgroups</span></a></p>
  <div class="sphx-glr-thumbnail-title">Multiclass sparse logistic regression on 20newgroups</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly pr..."><img alt="" src="../../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></p>
  <div class="sphx-glr-thumbnail-title">The Johnson-Lindenstrauss bound for embedding with random projections</div>
</div></div><div class="clearer"></div></section>
</section>


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