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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="#sklearn.datasets.fetch_california_housing"><code class="docutils literal notranslate"><span class="pre">fetch_california_housing</span></code></a></li>
<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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<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 heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_california_housing">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_california_housing</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">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">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/_california_housing.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></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">
<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<span class="colon">:</span></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 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>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<span class="colon">:</span></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">str</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><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>
<p class="rubric">Notes</p>
<p>This dataset consists of 20,640 samples and 9 features.</p>
</dd></dl>
<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 heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.24! Many bug fixes and improvements we..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_0_24_0_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 0.24</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the performance of Random Forest (RF) and Histogram Gradient Boostin..."><img alt="" src="../../_images/sphx_glr_plot_forest_hist_grad_boosting_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html#sphx-glr-auto-examples-ensemble-plot-forest-hist-grad-boosting-comparison-py"><span class="std std-ref">Comparing Random Forests and Histogram Gradient Boosting models</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing Random Forests and Histogram Gradient Boosting models</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..."><img alt="" src="../../_images/sphx_glr_plot_missing_values_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Imputing missing values before building an estimator</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..."><img alt="" src="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Imputing missing values with variants of IterativeImputer</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Feature 0 (median income in a block) and feature 5 (average house occupancy) of the california_..."><img alt="" src="../../_images/sphx_glr_plot_all_scaling_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Compare the effect of different scalers on data with outliers</div>
</div></div><div class="clearer"></div></section>
</section>
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