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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_california_housing</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-california-housing">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_california_housing</span></code></a></li>
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<div class="section" id="sklearn-datasets-fetch-california-housing">
<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_california_housing<a class="headerlink" href="#sklearn-datasets-fetch-california-housing" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="sklearn.datasets.fetch_california_housing">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">fetch_california_housing</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">data_home</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">download_if_missing</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">return_X_y</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">as_frame</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/datasets/_california_housing.py#L53"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.fetch_california_housing" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the California housing dataset (regression).</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 50%" />
<col style="width: 50%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>20640</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>8</p></td>
</tr>
<tr class="row-odd"><td><p>Features</p></td>
<td><p>real</p></td>
</tr>
<tr class="row-even"><td><p>Target</p></td>
<td><p>real 0.15 - 5.</p></td>
</tr>
</tbody>
</table>
<p>Read more in the <a class="reference internal" href="../../datasets/real_world.html#california-housing-dataset"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<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>
</dd>
<dt><strong>download_if_missing</strong><span class="classifier">bool, default=True</span></dt><dd><p>If False, raise a IOError 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>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 target_columns.</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</dt>
<dd class="field-even"><dl>
<dt><strong>dataset</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, shape (20640, 8)</span></dt><dd><p>Each row corresponding to the 8 feature values in order.
If <code class="docutils literal notranslate"><span class="pre">as_frame</span></code> is True, <code class="docutils literal notranslate"><span class="pre">data</span></code> is a pandas object.</p>
</dd>
<dt>target<span class="classifier">numpy array of shape (20640,)</span></dt><dd><p>Each value corresponds to the average
house value in units of 100,000.
If <code class="docutils literal notranslate"><span class="pre">as_frame</span></code> is True, <code class="docutils literal notranslate"><span class="pre">target</span></code> is a pandas object.</p>
</dd>
<dt>feature_names<span class="classifier">list of length 8</span></dt><dd><p>Array of ordered feature names used in the dataset.</p>
</dd>
<dt>DESCR<span class="classifier">string</span></dt><dd><p>Description of the California housing dataset.</p>
</dd>
<dt>frame<span class="classifier">pandas DataFrame</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>
</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><div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This dataset consists of 20,640 samples and 9 features.</p>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-fetch-california-housing">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_california_housing</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-california-housing" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.24! Many bug fixes and improvements we..."><div class="figure align-default" id="id1">
<img alt="Release Highlights for scikit-learn 0.24" src="../../_images/sphx_glr_plot_release_highlights_0_24_0_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_24_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-24-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.24</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Partial dependence plots show the dependence between the target function [2]_ and a set of feat..."><div class="figure align-default" id="id2">
<img alt="Partial Dependence and Individual Conditional Expectation Plots" src="../../_images/sphx_glr_plot_partial_dependence_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence and Individual Conditional Expectation Plots</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The IterativeImputer class is very flexible - it can be used with a variety of estimators to do..."><div class="figure align-default" id="id3">
<img alt="Imputing missing values with variants of IterativeImputer" src="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Missing values can be replaced by the mean, the median or the most frequent value using the bas..."><div class="figure align-default" id="id4">
<img alt="Imputing missing values before building an estimator" src="../../_images/sphx_glr_plot_missing_values_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Feature 0 (median income in a block) and feature 5 (number of households) of the california_hou..."><div class="figure align-default" id="id5">
<img alt="Compare the effect of different scalers on data with outliers" src="../../_images/sphx_glr_plot_all_scaling_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py"><span class="std std-ref">Compare the effect of different scalers on data with outliers</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
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