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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_diabetes</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.load_diabetes"><code class="docutils literal notranslate"><span class="pre">load_diabetes</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-load-diabetes">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_diabetes</span></code></a></li>
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<section id="sklearn-datasets-load-diabetes">
<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_diabetes<a class="headerlink" href="#sklearn-datasets-load-diabetes" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.load_diabetes">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_diabetes</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>, <em class="sig-param"><span class="n"><span class="pre">scaled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/datasets/_base.py#L1001"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_diabetes" title="Permalink to this definition">¶</a></dt>
<dd><p>Load and return the diabetes dataset (regression).</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>442</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>10</p></td>
</tr>
<tr class="row-odd"><td><p>Features</p></td>
<td><p>real, -.2 < x < .2</p></td>
</tr>
<tr class="row-even"><td><p>Targets</p></td>
<td><p>integer 25 - 346</p></td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The meaning of each feature (i.e. <code class="docutils literal notranslate"><span class="pre">feature_names</span></code>) might be unclear
(especially for <code class="docutils literal notranslate"><span class="pre">ltg</span></code>) as the documentation of the original dataset is
not explicit. We provide information that seems correct in regard with
the scientific literature in this field of research.</p>
</div>
<p>Read more in the <a class="reference internal" href="../../datasets/toy_dataset.html#diabetes-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>
<dt><strong>scaled</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, the feature variables are mean centered and scaled by the
standard deviation times the square root of <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>.
If False, raw data is returned for the feature variables.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.1.</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 (442, 10)</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 (442,)</dt><dd><p>The regression 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>frame: DataFrame of shape (442, 11)</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>
<dt>data_filename: str</dt><dd><p>The path to the location of the data.</p>
</dd>
<dt>target_filename: str</dt><dd><p>The path to the location of the target.</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>Returns a tuple of two ndarray of shape (n_samples, n_features)
A 2D array with each row representing one sample and each column
representing the features and/or target of a given sample.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<section id="examples-using-sklearn-datasets-load-diabetes">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_diabetes</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-load-diabetes" 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 1.2! Many bug fixes and improvements wer..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_2_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_2_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-2-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.2</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.2</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of w..."><img alt="" src="../../_images/sphx_glr_plot_gradient_boosting_regression_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py"><span class="std std-ref">Gradient Boosting regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the..."><img alt="" src="../../_images/sphx_glr_plot_voting_regressor_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot individual and voting regression predictions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrate how model complexity influences both prediction accuracy and computational performa..."><img alt="" src="../../_images/sphx_glr_plot_model_complexity_influence_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py"><span class="std std-ref">Model Complexity Influence</span></a></p>
<div class="sphx-glr-thumbnail-title">Model Complexity Influence</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="Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent."><img alt="" src="../../_images/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py"><span class="std std-ref">Lasso and Elastic Net</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso and Elastic Net</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example reproduces the example of Fig. 2 of [ZHT2007]_. A LassoLarsIC estimator is fit on ..."><img alt="" src="../../_images/sphx_glr_plot_lasso_lars_ic_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_lars_ic.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-ic-py"><span class="std std-ref">Lasso model selection via information criteria</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso model selection via information criteria</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example focuses on model selection for Lasso models that are linear models with an L1 pena..."><img alt="" src="../../_images/sphx_glr_plot_lasso_model_selection_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py"><span class="std std-ref">Lasso model selection: AIC-BIC / cross-validation</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso model selection: AIC-BIC / cross-validation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes..."><img alt="" src="../../_images/sphx_glr_plot_lasso_lars_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py"><span class="std std-ref">Lasso path using LARS</span></a></p>
<div class="sphx-glr-thumbnail-title">Lasso path using LARS</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The coefficients, residual sum of squares and the coefficient of determination are also calcula..."><img alt="" src="../../_images/sphx_glr_plot_ols_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py"><span class="std std-ref">Linear Regression Example</span></a></p>
<div class="sphx-glr-thumbnail-title">Linear Regression Example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that alth..."><img alt="" src="../../_images/sphx_glr_plot_ols_3d_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ols_3d.html#sphx-glr-auto-examples-linear-model-plot-ols-3d-py"><span class="std std-ref">Sparsity Example: Fitting only features 1 and 2</span></a></p>
<div class="sphx-glr-thumbnail-title">Sparsity Example: Fitting only features 1 and 2</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" See also sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py"><img alt="" src="../../_images/sphx_glr_plot_partial_dependence_visualization_api_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-partial-dependence-visualization-api-py"><span class="std std-ref">Advanced Plotting With Partial Dependence</span></a></p>
<div class="sphx-glr-thumbnail-title">Advanced Plotting With Partial Dependence</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="This example shows how to use cross_val_predict together with PredictionErrorDisplay to visuali..."><img alt="" src="../../_images/sphx_glr_plot_cv_predict_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py"><span class="std std-ref">Plotting Cross-Validated Predictions</span></a></p>
<div class="sphx-glr-thumbnail-title">Plotting Cross-Validated Predictions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise which uses cross-validation with linear models."><img alt="" src="../../_images/sphx_glr_plot_cv_diabetes_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/exercises/plot_cv_diabetes.html#sphx-glr-auto-examples-exercises-plot-cv-diabetes-py"><span class="std std-ref">Cross-validation on diabetes Dataset Exercise</span></a></p>
<div class="sphx-glr-thumbnail-title">Cross-validation on diabetes Dataset Exercise</div>
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