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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><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"><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"><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="function">
<dt id="sklearn.datasets.load_digits">
<code class="descclassname">sklearn.datasets.</code><code class="descname">load_digits</code><span class="sig-paren">(</span><em>n_class=10</em>, <em>return_X_y=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/datasets/base.py#L485"><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 border="1" class="docutils">
<colgroup>
<col width="55%" />
<col width="45%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td>Classes</td>
<td>10</td>
</tr>
<tr class="row-even"><td>Samples per class</td>
<td>~180</td>
</tr>
<tr class="row-odd"><td>Samples total</td>
<td>1797</td>
</tr>
<tr class="row-even"><td>Dimensionality</td>
<td>64</td>
</tr>
<tr class="row-odd"><td>Features</td>
<td>integers 0-16</td>
</tr>
</tbody>
</table>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#datasets"><span class="std std-ref">User Guide</span></a>.</p>
<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">Parameters:</th><td class="field-body"><p class="first"><strong>n_class</strong> : integer, between 0 and 10, optional (default=10)</p>
<blockquote>
<div><p>The number of classes to return.</p>
</div></blockquote>
<p><strong>return_X_y</strong> : boolean, default=False.</p>
<blockquote>
<div><p>If True, returns <code class="docutils literal"><span class="pre">(data,</span> <span class="pre">target)</span></code> instead of a Bunch object.
See below for more information about the <cite>data</cite> and <cite>target</cite> object.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.18.</span></p>
</div>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> : Bunch</p>
<blockquote>
<div><p>Dictionary-like object, the interesting attributes are:
‘data’, the data to learn, ‘images’, the images corresponding
to each sample, ‘target’, the classification labels for each
sample, ‘target_names’, the meaning of the labels, and ‘DESCR’,
the full description of the dataset.</p>
</div></blockquote>
<p><strong>(data, target)</strong> : tuple if <code class="docutils literal"><span class="pre">return_X_y</span></code> is True</p>
<blockquote>
<div><div class="versionadded">
<p><span class="versionmodified">New in version 0.18.</span></p>
</div>
</div></blockquote>
<p>This is a copy of the test set of the UCI ML hand-written digits datasets</p>
<p class="last"><a class="reference external" href="http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits">http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits</a></p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<p>To load the data and visualize the images:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">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"><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="The PCA does an unsupervised dimensionality reduction, while the logistic regression does the p..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_digits_pipe_thumb.png" src="../../_images/sphx_glr_plot_digits_pipe_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_digits_pipe.html#sphx-glr-auto-examples-plot-digits-pipe-py"><span class="std std-ref">Pipelining: chaining a PCA and a logistic regression</span></a></span></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" id="id2">
<img alt="../../_images/sphx_glr_plot_compare_reduction_thumb.png" src="../../_images/sphx_glr_plot_compare_reduction_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_compare_reduction.html#sphx-glr-auto-examples-plot-compare-reduction-py"><span class="std std-ref">Selecting dimensionality reduction with Pipeline and GridSearchCV</span></a></span></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" id="id3">
<img alt="../../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" src="../../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></span></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" id="id4">
<img alt="../../_images/sphx_glr_plot_kernel_approximation_thumb.png" src="../../_images/sphx_glr_plot_kernel_approximation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_kernel_approximation.html#sphx-glr-auto-examples-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example showing how the scikit-learn can be used to recognize images of hand-written digits."><div class="figure" id="id5">
<img alt="../../_images/sphx_glr_plot_digits_classification_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="These images how similar features are merged together using feature agglomeration. "><div class="figure" id="id6">
<img alt="../../_images/sphx_glr_plot_digits_agglomeration_thumb.png" 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></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" id="id7">
<img alt="../../_images/sphx_glr_plot_digits_linkage_thumb.png" 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></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" id="id8">
<img alt="../../_images/sphx_glr_plot_kmeans_digits_thumb.png" 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></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" id="id9">
<img alt="../../_images/sphx_glr_plot_digits_last_image_thumb.png" 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></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" id="id10">
<img alt="../../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" 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></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" id="id11">
<img alt="../../_images/sphx_glr_plot_digits_classification_exercise_thumb.png" 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></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" id="id12">
<img alt="../../_images/sphx_glr_plot_cv_digits_thumb.png" 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></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" id="id13">
<img alt="../../_images/sphx_glr_plot_rfe_digits_thumb.png" 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></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" id="id14">
<img alt="../../_images/sphx_glr_plot_sgd_comparison_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of the sparsity (percentage of zero coefficients) of solutions when L1 and L2 penalt..."><div class="figure" id="id15">
<img alt="../../_images/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of various embeddings on the digits dataset."><div class="figure" id="id16">
<img alt="../../_images/sphx_glr_plot_lle_digits_thumb.png" 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></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" id="id17">
<img alt="../../_images/sphx_glr_plot_validation_curve_thumb.png" 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></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" id="id18">
<img alt="../../_images/sphx_glr_plot_grid_search_digits_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Compare randomized search and grid search for optimizing hyperparameters of a random forest. Al..."><div class="figure" id="id19">
<img alt="../../_images/sphx_glr_plot_randomized_search_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="On the left side the learning curve of a naive Bayes classifier is shown for the digits dataset..."><div class="figure" id="id20">
<img alt="../../_images/sphx_glr_plot_learning_curve_thumb.png" 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></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" id="id21">
<img alt="../../_images/sphx_glr_plot_digits_kde_sampling_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example visualizes some training loss curves for different stochastic learning strategies,..."><div class="figure" id="id22">
<img alt="../../_images/sphx_glr_plot_mlp_training_curves_thumb.png" 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></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" id="id23">
<img alt="../../_images/sphx_glr_plot_rbm_logistic_classification_thumb.png" 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></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" id="id24">
<img alt="../../_images/sphx_glr_plot_label_propagation_digits_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><div class="figure" id="id25">
<img alt="../../_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" 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></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform univariate feature selection before running a SVC (support ve..."><div class="figure" id="id26">
<img alt="../../_images/sphx_glr_plot_svm_anova_thumb.png" src="../../_images/sphx_glr_plot_svm_anova_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/svm/plot_svm_anova.html#sphx-glr-auto-examples-svm-plot-svm-anova-py"><span class="std std-ref">SVM-Anova: SVM with univariate feature selection</span></a></span></p>
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