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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>.load_wine</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.load_wine"><code class="docutils literal notranslate"><span class="pre">load_wine</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-load-wine">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_wine</span></code></a></li>
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<section id="sklearn-datasets-load-wine">
<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>.load_wine<a class="headerlink" href="#sklearn-datasets-load-wine" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.load_wine">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_wine</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">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/3f89022fa/sklearn/datasets/_base.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_wine" title="Permalink to this definition">¶</a></dt>
<dd><p>Load and return the wine dataset (classification).</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
<p>The wine dataset is a classic and very easy multi-class classification
dataset.</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>3</p></td>
</tr>
<tr class="row-even"><td><p>Samples per class</p></td>
<td><p>[59,71,48]</p></td>
</tr>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>178</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>13</p></td>
</tr>
<tr class="row-odd"><td><p>Features</p></td>
<td><p>real, positive</p></td>
</tr>
</tbody>
</table>
<p>The copy of UCI ML Wine Data Set dataset is downloaded and modified to fit
standard format from:
<a class="reference external" href="https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data">https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data</a></p>
<p>Read more in the <a class="reference internal" href="../../datasets/toy_dataset.html#wine-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>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>
</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). The target is
a pandas DataFrame or Series depending on the number of target columns.
If <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True, then (<code class="docutils literal notranslate"><span class="pre">data</span></code>, <code class="docutils literal notranslate"><span class="pre">target</span></code>) will be pandas
DataFrames or Series as described below.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.23.</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>
<dt>data<span class="classifier">{ndarray, dataframe} of shape (178, 13)</span></dt><dd><p>The data matrix. 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: {ndarray, Series} of shape (178,)</dt><dd><p>The classification target. 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>feature_names: list</dt><dd><p>The names of the dataset columns.</p>
</dd>
<dt>target_names: list</dt><dd><p>The names of target classes.</p>
</dd>
<dt>frame: DataFrame of shape (178, 14)</dt><dd><p>Only present when <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>. 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 0.23.</span></p>
</div>
</dd>
<dt>DESCR: str</dt><dd><p>The full description of the dataset.</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 ndarrays by default. The first contains a 2D array of shape
(178, 13) with each row representing one sample and each column representing
the features. The second array of shape (178,) contains the target samples.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>Let’s say you are interested in the samples 10, 80, and 140, and want to
know their class name.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_wine</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">load_wine</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">data</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="mi">80</span><span class="p">,</span> <span class="mi">140</span><span class="p">]]</span>
<span class="go">array([0, 1, 2])</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="go">['class_0', 'class_1', 'class_2']</span>
</pre></div>
</div>
</dd></dl>
<section id="examples-using-sklearn-datasets-load-wine">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_wine</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-load-wine" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is us..."><img alt="" src="../../_images/sphx_glr_plot_outlier_detection_wine_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_outlier_detection_wine.html#sphx-glr-auto-examples-applications-plot-outlier-detection-wine-py"><span class="std std-ref">Outlier detection on a real data set</span></a></p>
<div class="sphx-glr-thumbnail-title">Outlier detection on a real data set</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="ROC Curve with Visualization API"><img alt="" src="../../_images/sphx_glr_plot_roc_curve_visualization_api_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p>
<div class="sphx-glr-thumbnail-title">ROC Curve with Visualization API</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Feature scaling through standardization, also called Z-score normalization, is an important pre..."><img alt="" src="../../_images/sphx_glr_plot_scaling_importance_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py"><span class="std std-ref">Importance of Feature Scaling</span></a></p>
<div class="sphx-glr-thumbnail-title">Importance of Feature Scaling</div>
</div></div><div class="clearer"></div></section>
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