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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.load_boston</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-load-boston">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.load_boston</span></code></a></li>
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<div class="section" id="sklearn-datasets-load-boston">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>.load_boston<a class="headerlink" href="#sklearn-datasets-load-boston" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.load_boston">
<code class="descclassname">sklearn.datasets.</code><code class="descname">load_boston</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/datasets/base.py#L505"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.load_boston" title="Permalink to this definition">¶</a></dt>
<dd><p>Load and return the boston house-prices dataset (regression).</p>
<table border="1" class="docutils">
<colgroup>
<col width="50%" />
<col width="50%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td>Samples total</td>
<td>506</td>
</tr>
<tr class="row-even"><td>Dimensionality</td>
<td>13</td>
</tr>
<tr class="row-odd"><td>Features</td>
<td>real, positive</td>
</tr>
<tr class="row-even"><td>Targets</td>
<td>real 5. - 50.</td>
</tr>
</tbody>
</table>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> : Bunch</p>
<blockquote class="last">
<div><p>Dictionary-like object, the interesting attributes are:
‘data’, the data to learn, ‘target’, the regression targets,
and ‘DESCR’, the full description of the dataset.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_boston</span>
<span class="gp">>>> </span><span class="n">boston</span> <span class="o">=</span> <span class="n">load_boston</span><span class="p">()</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">boston</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(506, 13)</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-load-boston">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.load_boston</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-load-boston" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="This example shows how to use `cross_val_predict` to visualize prediction errors."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/./plot_cv_predict.html"><img alt="../../_images/plot_cv_predict1.png" src="../../_images/plot_cv_predict1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_cv_predict.html#example-plot-cv-predict-py"><span>Plotting Cross-Validated Predictions</span></a></span></p>
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</div><div class="thumbnailContainer" tooltip="This example shows that imputing the missing values can give better results than discarding the..."><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/./missing_values.html"><img alt="../../_images/missing_values1.png" src="../../_images/missing_values1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/missing_values.html#example-missing-values-py"><span>Imputing missing values before building an estimator</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is us..."><div class="figure" id="id3">
<a class="reference external image-reference" href="./../../auto_examples/applications/plot_outlier_detection_housing.html"><img alt="../../_images/plot_outlier_detection_housing1.png" src="../../_images/plot_outlier_detection_housing1.png" /></a>
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</div><div class="thumbnailContainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performa..."><div class="figure" id="id4">
<a class="reference external image-reference" href="./../../auto_examples/applications/plot_model_complexity_influence.html"><img alt="../../_images/plot_model_complexity_influence1.png" src="../../_images/plot_model_complexity_influence1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/plot_model_complexity_influence.html#example-applications-plot-model-complexity-influence-py"><span>Model Complexity Influence</span></a></span></p>
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</div><div class="thumbnailContainer" tooltip="Demonstrate Gradient Boosting on the Boston housing dataset."><div class="figure" id="id5">
<a class="reference external image-reference" href="./../../auto_examples/ensemble/plot_gradient_boosting_regression.html"><img alt="../../_images/plot_gradient_boosting_regression1.png" src="../../_images/plot_gradient_boosting_regression1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_regression.html#example-ensemble-plot-gradient-boosting-regression-py"><span>Gradient Boosting regression</span></a></span></p>
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</div><div class="thumbnailContainer" tooltip="Use SelectFromModel meta-transformer along with Lasso to select the best couple of features fro..."><div class="figure" id="id6">
<a class="reference external image-reference" href="./../../auto_examples/feature_selection/plot_select_from_model_boston.html"><img alt="../../_images/plot_select_from_model_boston1.png" src="../../_images/plot_select_from_model_boston1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/feature_selection/plot_select_from_model_boston.html#example-feature-selection-plot-select-from-model-boston-py"><span>Feature selection using SelectFromModel and LassoCV</span></a></span></p>
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