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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_digits</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-load-digits">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_digits</span></code></a></li>
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<div class="section" id="sklearn-datasets-load-digits">
<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_digits<a class="headerlink" href="#sklearn-datasets-load-digits" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="sklearn.datasets.load_digits">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">load_digits</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">n_class</span><span class="o">=</span><span class="default_value">10</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/_base.py#L635"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.load_digits" title="Permalink to this definition">¶</a></dt>
<dd><p>Load and return the digits dataset (classification).</p>
<p>Each datapoint is a 8x8 image of a digit.</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 55%" />
<col style="width: 45%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>10</p></td>
</tr>
<tr class="row-even"><td><p>Samples per class</p></td>
<td><p>~180</p></td>
</tr>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>1797</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>64</p></td>
</tr>
<tr class="row-odd"><td><p>Features</p></td>
<td><p>integers 0-16</p></td>
</tr>
</tbody>
</table>
<p>Read more in the <a class="reference internal" href="../../datasets/toy_dataset.html#digits-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>n_class</strong><span class="classifier">int, default=10</span></dt><dd><p>The number of classes to return. Between 0 and 10.</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,</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</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 (1797, 64)</span></dt><dd><p>The flattened 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 (1797,)</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: list</dt><dd><p>The names of the dataset columns.</p>
</dd>
<dt>target_names: list</dt><dd><p>The names of target classes.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
<dt>frame: DataFrame of shape (1797, 65)</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>images: {ndarray} of shape (1797, 8, 8)</dt><dd><p>The raw image data.</p>
</dd>
<dt>DESCR: str</dt><dd><p>The full description of the dataset.</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><div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
</dd>
<dt>This is a copy of the test set of the UCI ML hand-written digits datasets</dt><dd></dd>
<dt><a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits</a></dt><dd></dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>To load the data and visualize the images:</p>
<div class="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_digits</span>
<span class="gp">>>> </span><span class="n">digits</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">digits</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">(1797, 64)</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">gray</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-load-digits">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.load_digits</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-load-digits" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example shows how scikit-learn can be used to recognize images of hand-written digits, fro..."><div class="figure align-default" id="id1">
<img alt="Recognizing hand-written digits" src="../../_images/sphx_glr_plot_digits_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py"><span class="std std-ref">Recognizing hand-written digits</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="These images how similar features are merged together using feature agglomeration."><div class="figure align-default" id="id2">
<img alt="Feature agglomeration" src="../../_images/sphx_glr_plot_digits_agglomeration_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py"><span class="std std-ref">Feature agglomeration</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various linkage option for agglomerative clustering on a 2D embedding of the..."><div class="figure align-default" id="id3">
<img alt="Various Agglomerative Clustering on a 2D embedding of digits" src="../../_images/sphx_glr_plot_digits_linkage_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py"><span class="std std-ref">Various Agglomerative Clustering on a 2D embedding of digits</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the various initialization strategies for K-means in terms of runtim..."><div class="figure align-default" id="id4">
<img alt="A demo of K-Means clustering on the handwritten digits data" src="../../_images/sphx_glr_plot_kmeans_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py"><span class="std std-ref">A demo of K-Means clustering on the handwritten digits data</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This dataset is made up of 1797 8x8 images. Each image, like the one shown below, is of a hand-..."><div class="figure align-default" id="id5">
<img alt="The Digit Dataset" src="../../_images/sphx_glr_plot_digits_last_image_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/datasets/plot_digits_last_image.html#sphx-glr-auto-examples-datasets-plot-digits-last-image-py"><span class="std std-ref">The Digit Dataset</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient boosting is an ensembling technique where several weak learners (regression trees) are..."><div class="figure align-default" id="id6">
<img alt="Early stopping of Gradient Boosting" src="../../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">Early stopping of Gradient Boosting</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A recursive feature elimination example showing the relevance of pixels in a digit classificati..."><div class="figure align-default" id="id7">
<img alt="Recursive feature elimination" src="../../_images/sphx_glr_plot_rfe_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py"><span class="std std-ref">Recursive feature elimination</span></a></span><a class="headerlink" href="#id7" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example showing how different online solvers perform on the hand-written digits dataset."><div class="figure align-default" id="id8">
<img alt="Comparing various online solvers" src="../../_images/sphx_glr_plot_sgd_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py"><span class="std std-ref">Comparing various online solvers</span></a></span><a class="headerlink" href="#id8" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elast..."><div class="figure align-default" id="id9">
<img alt="L1 Penalty and Sparsity in Logistic Regression" src="../../_images/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py"><span class="std std-ref">L1 Penalty and Sparsity in Logistic Regression</span></a></span><a class="headerlink" href="#id9" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various embeddings on the digits dataset."><div class="figure align-default" id="id10">
<img alt="Manifold learning on handwritten digits: Locally Linear Embedding, Isomap..." src="../../_images/sphx_glr_plot_lle_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py"><span class="std std-ref">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</span></a></span><a class="headerlink" href="#id10" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly pr..."><div class="figure align-default" id="id11">
<img alt="The Johnson-Lindenstrauss bound for embedding with random projections" src="../../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></span><a class="headerlink" href="#id11" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example illustrating the approximation of the feature map of an RBF kernel."><div class="figure align-default" id="id12">
<img alt="Explicit feature map approximation for RBF kernels" src="../../_images/sphx_glr_plot_kernel_approximation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/miscellaneous/plot_kernel_approximation.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a></span><a class="headerlink" href="#id12" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this plot you can see the training scores and validation scores of an SVM for different valu..."><div class="figure align-default" id="id13">
<img alt="Plotting Validation Curves" src="../../_images/sphx_glr_plot_validation_curve_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py"><span class="std std-ref">Plotting Validation Curves</span></a></span><a class="headerlink" href="#id13" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This examples shows how a classifier is optimized by cross-validation, which is done using the ..."><div class="figure align-default" id="id14">
<img alt="Parameter estimation using grid search with cross-validation" src="../../_images/sphx_glr_plot_grid_search_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py"><span class="std std-ref">Parameter estimation using grid search with cross-validation</span></a></span><a class="headerlink" href="#id14" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with S..."><div class="figure align-default" id="id15">
<img alt="Comparing randomized search and grid search for hyperparameter estimation" src="../../_images/sphx_glr_plot_randomized_search_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py"><span class="std std-ref">Comparing randomized search and grid search for hyperparameter estimation</span></a></span><a class="headerlink" href="#id15" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example balances model complexity and cross-validated score by finding a decent accuracy w..."><div class="figure align-default" id="id16">
<img alt="Balance model complexity and cross-validated score" src="../../_images/sphx_glr_plot_grid_search_refit_callable_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py"><span class="std std-ref">Balance model complexity and cross-validated score</span></a></span><a class="headerlink" href="#id16" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plotting Learning Curves"><div class="figure align-default" id="id17">
<img alt="Plotting Learning Curves" src="../../_images/sphx_glr_plot_learning_curve_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py"><span class="std std-ref">Plotting Learning Curves</span></a></span><a class="headerlink" href="#id17" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how kernel density estimation (KDE), a powerful non-parametric density estim..."><div class="figure align-default" id="id18">
<img alt="Kernel Density Estimation" src="../../_images/sphx_glr_plot_digits_kde_sampling_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py"><span class="std std-ref">Kernel Density Estimation</span></a></span><a class="headerlink" href="#id18" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This examples demonstrates how to precompute the k nearest neighbors before using them in KNeig..."><div class="figure align-default" id="id19">
<img alt="Caching nearest neighbors" src="../../_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py"><span class="std std-ref">Caching nearest neighbors</span></a></span><a class="headerlink" href="#id19" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sample usage of Neighborhood Components Analysis for dimensionality reduction."><div class="figure align-default" id="id20">
<img alt="Dimensionality Reduction with Neighborhood Components Analysis" src="../../_images/sphx_glr_plot_nca_dim_reduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py"><span class="std std-ref">Dimensionality Reduction with Neighborhood Components Analysis</span></a></span><a class="headerlink" href="#id20" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="For greyscale image data where pixel values can be interpreted as degrees of blackness on a whi..."><div class="figure align-default" id="id21">
<img alt="Restricted Boltzmann Machine features for digit classification" src="../../_images/sphx_glr_plot_rbm_logistic_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py"><span class="std std-ref">Restricted Boltzmann Machine features for digit classification</span></a></span><a class="headerlink" href="#id21" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example visualizes some training loss curves for different stochastic learning strategies,..."><div class="figure align-default" id="id22">
<img alt="Compare Stochastic learning strategies for MLPClassifier" src="../../_images/sphx_glr_plot_mlp_training_curves_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py"><span class="std std-ref">Compare Stochastic learning strategies for MLPClassifier</span></a></span><a class="headerlink" href="#id22" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The PCA does an unsupervised dimensionality reduction, while the logistic regression does the p..."><div class="figure align-default" id="id23">
<img alt="Pipelining: chaining a PCA and a logistic regression" src="../../_images/sphx_glr_plot_digits_pipe_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/compose/plot_digits_pipe.html#sphx-glr-auto-examples-compose-plot-digits-pipe-py"><span class="std std-ref">Pipelining: chaining a PCA and a logistic regression</span></a></span><a class="headerlink" href="#id23" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example constructs a pipeline that does dimensionality reduction followed by prediction wi..."><div class="figure align-default" id="id24">
<img alt="Selecting dimensionality reduction with Pipeline and GridSearchCV" src="../../_images/sphx_glr_plot_compare_reduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py"><span class="std std-ref">Selecting dimensionality reduction with Pipeline and GridSearchCV</span></a></span><a class="headerlink" href="#id24" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the power of semisupervised learning by training a Label Spreading mo..."><div class="figure align-default" id="id25">
<img alt="Label Propagation digits: Demonstrating performance" src="../../_images/sphx_glr_plot_label_propagation_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/semi_supervised/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py"><span class="std std-ref">Label Propagation digits: Demonstrating performance</span></a></span><a class="headerlink" href="#id25" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><div class="figure align-default" id="id26">
<img alt="Label Propagation digits active learning" src="../../_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></span><a class="headerlink" href="#id26" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise regarding the use of classification techniques on the Digits dataset."><div class="figure align-default" id="id27">
<img alt="Digits Classification Exercise" src="../../_images/sphx_glr_plot_digits_classification_exercise_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/exercises/plot_digits_classification_exercise.html#sphx-glr-auto-examples-exercises-plot-digits-classification-exercise-py"><span class="std std-ref">Digits Classification Exercise</span></a></span><a class="headerlink" href="#id27" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A tutorial exercise using Cross-validation with an SVM on the Digits dataset."><div class="figure align-default" id="id28">
<img alt="Cross-validation on Digits Dataset Exercise" src="../../_images/sphx_glr_plot_cv_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/exercises/plot_cv_digits.html#sphx-glr-auto-examples-exercises-plot-cv-digits-py"><span class="std std-ref">Cross-validation on Digits Dataset Exercise</span></a></span><a class="headerlink" href="#id28" title="Permalink to this image">¶</a></p>
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