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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_breast_cancer</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.load_breast_cancer"><code class="docutils literal notranslate"><span class="pre">load_breast_cancer</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-load-breast-cancer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_breast_cancer</span></code></a></li>
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<section id="sklearn-datasets-load-breast-cancer">
<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_breast_cancer<a class="headerlink" href="#sklearn-datasets-load-breast-cancer" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.load_breast_cancer">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_breast_cancer</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#L710"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_breast_cancer" title="Permalink to this definition">¶</a></dt>
<dd><p>Load and return the breast cancer wisconsin dataset (classification).</p>
<p>The breast cancer dataset is a classic and very easy binary classification
dataset.</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>2</p></td>
</tr>
<tr class="row-even"><td><p>Samples per class</p></td>
<td><p>212(M),357(B)</p></td>
</tr>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>569</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>30</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 Breast Cancer Wisconsin (Diagnostic) dataset is
downloaded from:
<a class="reference external" href="https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic">https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic</a></p>
<p>Read more in the <a class="reference internal" href="../../datasets/toy_dataset.html#breast-cancer-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>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</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). 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 (569, 30)</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<span class="classifier">{ndarray, Series} of shape (569,)</span></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<span class="classifier">ndarray of shape (30,)</span></dt><dd><p>The names of the dataset columns.</p>
</dd>
<dt>target_names<span class="classifier">ndarray of shape (2,)</span></dt><dd><p>The names of target classes.</p>
</dd>
<dt>frame<span class="classifier">DataFrame of shape (569, 31)</span></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<span class="classifier">str</span></dt><dd><p>The full description of the dataset.</p>
</dd>
<dt>filename<span class="classifier">str</span></dt><dd><p>The path to the location of the data.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</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>A tuple of two ndarrays by default. The first contains a 2D ndarray of
shape (569, 30) with each row representing one sample and each column
representing the features. The second ndarray of shape (569,) contains
the target samples. If <code class="docutils literal notranslate"><span class="pre">as_frame=True</span></code>, both arrays are pandas objects,
i.e. <code class="docutils literal notranslate"><span class="pre">X</span></code> a dataframe and <code class="docutils literal notranslate"><span class="pre">y</span></code> a series.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>Let’s say you are interested in the samples 10, 50, and 85, 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_breast_cancer</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">load_breast_cancer</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">50</span><span class="p">,</span> <span class="mi">85</span><span class="p">]]</span>
<span class="go">array([0, 1, 0])</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">['malignant', 'benign']</span>
</pre></div>
</div>
</dd></dl>
<section id="examples-using-sklearn-datasets-load-breast-cancer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_breast_cancer</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-load-breast-cancer" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to preven..."><img alt="" src="../../_images/sphx_glr_plot_cost_complexity_pruning_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py"><span class="std std-ref">Post pruning decision trees with cost complexity pruning</span></a></p>
<div class="sphx-glr-thumbnail-title">Post pruning decision trees with cost complexity pruning</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares two approaches for feature selection: SelectFromModel whi..."><img alt="" src="../../_images/sphx_glr_plot_select_from_model_diabetes_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/feature_selection/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py"><span class="std std-ref">Model-based and sequential feature selection</span></a></p>
<div class="sphx-glr-thumbnail-title">Model-based and sequential feature selection</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compute the permutation_importance of the features to a trained RandomFores..."><img alt="" src="../../_images/sphx_glr_plot_permutation_importance_multicollinear_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_permutation_importance_multicollinear.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-multicollinear-py"><span class="std std-ref">Permutation Importance with Multicollinear or Correlated Features</span></a></p>
<div class="sphx-glr-thumbnail-title">Permutation Importance with Multicollinear or Correlated Features</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of a varying threshold on self-training. The breast_cancer ..."><img alt="" src="../../_images/sphx_glr_plot_self_training_varying_threshold_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/semi_supervised/plot_self_training_varying_threshold.html#sphx-glr-auto-examples-semi-supervised-plot-self-training-varying-threshold-py"><span class="std std-ref">Effect of varying threshold for self-training</span></a></p>
<div class="sphx-glr-thumbnail-title">Effect of varying threshold for self-training</div>
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