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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.fetch_kddcup99</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.fetch_kddcup99"><code class="docutils literal notranslate"><span class="pre">fetch_kddcup99</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-kddcup99">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_kddcup99</span></code></a></li>
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<section id="sklearn-datasets-fetch-kddcup99">
<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_kddcup99<a class="headerlink" href="#sklearn-datasets-fetch-kddcup99" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_kddcup99">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_kddcup99</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">None</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">shuffle</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">random_state</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">percent10</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">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">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/_kddcup99.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_kddcup99" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the kddcup99 dataset (classification).</p>
<p>Download it if necessary.</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>23</p></td>
</tr>
<tr class="row-even"><td><p>Samples total</p></td>
<td><p>4898431</p></td>
</tr>
<tr class="row-odd"><td><p>Dimensionality</p></td>
<td><p>41</p></td>
</tr>
<tr class="row-even"><td><p>Features</p></td>
<td><p>discrete (int) or continuous (float)</p></td>
</tr>
</tbody>
</table>
<p>Read more in the <a class="reference internal" href="../../datasets/real_world.html#kddcup99-dataset"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
<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">{‘SA’, ‘SF’, ‘http’, ‘smtp’}, default=None</span></dt><dd><p>To return the corresponding classical subsets of kddcup 99.
If None, return the entire kddcup 99 dataset.</p>
</dd>
<dt><strong>data_home</strong><span class="classifier">str, default=None</span></dt><dd><p>Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to shuffle dataset.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines random number generation for dataset shuffling and for
selection of abnormal samples if <code class="docutils literal notranslate"><span class="pre">subset='SA'</span></code>. 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>percent10</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to load only 10 percent of the data.</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,</span> <span class="pre">target)</span></code> instead of a Bunch object. See
below for more information about the <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code> object.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
<dt><strong>as_frame</strong><span class="classifier">bool, default=False</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, returns a pandas Dataframe for the <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code>
objects in the <code class="docutils literal notranslate"><span class="pre">Bunch</span></code> returned object; <code class="docutils literal notranslate"><span class="pre">Bunch</span></code> return object will also
have a <code class="docutils literal notranslate"><span class="pre">frame</span></code> member.</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>data</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">{ndarray, dataframe} of shape (494021, 41)</span></dt><dd><p>The data matrix to learn. If <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>, <code class="docutils literal notranslate"><span class="pre">data</span></code> will be a
pandas DataFrame.</p>
</dd>
<dt>target<span class="classifier">{ndarray, series} of shape (494021,)</span></dt><dd><p>The regression target for each sample. If <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>, <code class="docutils literal notranslate"><span class="pre">target</span></code>
will be a pandas Series.</p>
</dd>
<dt>frame<span class="classifier">dataframe of shape (494021, 42)</span></dt><dd><p>Only present when <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>. Contains <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
</dd>
<dt>DESCR<span class="classifier">str</span></dt><dd><p>The full description of the dataset.</p>
</dd>
<dt>feature_names<span class="classifier">list</span></dt><dd><p>The names of the dataset columns</p>
</dd>
<dt>target_names: list</dt><dd><p>The names of the target columns</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</span></code> is True</span></dt><dd><p>A tuple of two ndarray. The first containing a 2D array of
shape (n_samples, n_features) with each row representing one
sample and each column representing the features. The second
ndarray of shape (n_samples,) containing the target samples.</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-kddcup99">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_kddcup99</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-kddcup99" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example benchmarks outlier detection algorithms, local_outlier_factor (LOF) and isolation_..."><img alt="" src="../../_images/sphx_glr_plot_outlier_detection_bench_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_outlier_detection_bench.html#sphx-glr-auto-examples-miscellaneous-plot-outlier-detection-bench-py"><span class="std std-ref">Evaluation of outlier detection estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Evaluation of outlier detection estimators</div>
</div></div><div class="clearer"></div></section>
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