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<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../visualizations.html">5. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="loading_other_datasets.html">7.4. Loading other datasets</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="real-world-datasets">
<span id="id1"></span><h1><span class="section-number">7.2. </span>Real world datasets<a class="headerlink" href="#real-world-datasets" title="Link to this heading">#</a></h1>
<p>scikit-learn provides tools to load larger datasets, downloading them if
necessary.</p>
<p>They can be loaded using the following functions:</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_olivetti_faces.html#sklearn.datasets.fetch_olivetti_faces" title="sklearn.datasets.fetch_olivetti_faces"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_olivetti_faces</span></code></a>(*[, data_home, ...])</p></td>
<td><p>Load the Olivetti faces data-set from AT&T (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_20newsgroups</span></code></a>(*[, data_home, subset, ...])</p></td>
<td><p>Load the filenames and data from the 20 newsgroups dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_20newsgroups_vectorized.html#sklearn.datasets.fetch_20newsgroups_vectorized" title="sklearn.datasets.fetch_20newsgroups_vectorized"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_20newsgroups_vectorized</span></code></a>(*[, subset, ...])</p></td>
<td><p>Load and vectorize the 20 newsgroups dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" title="sklearn.datasets.fetch_lfw_people"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_lfw_people</span></code></a>(*[, data_home, funneled, ...])</p></td>
<td><p>Load the Labeled Faces in the Wild (LFW) people dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_lfw_pairs.html#sklearn.datasets.fetch_lfw_pairs" title="sklearn.datasets.fetch_lfw_pairs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_lfw_pairs</span></code></a>(*[, subset, data_home, ...])</p></td>
<td><p>Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_covtype.html#sklearn.datasets.fetch_covtype" title="sklearn.datasets.fetch_covtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_covtype</span></code></a>(*[, data_home, ...])</p></td>
<td><p>Load the covertype dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_rcv1.html#sklearn.datasets.fetch_rcv1" title="sklearn.datasets.fetch_rcv1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_rcv1</span></code></a>(*[, data_home, subset, ...])</p></td>
<td><p>Load the RCV1 multilabel dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_kddcup99.html#sklearn.datasets.fetch_kddcup99" title="sklearn.datasets.fetch_kddcup99"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_kddcup99</span></code></a>(*[, subset, data_home, ...])</p></td>
<td><p>Load the kddcup99 dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_california_housing.html#sklearn.datasets.fetch_california_housing" title="sklearn.datasets.fetch_california_housing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_california_housing</span></code></a>(*[, data_home, ...])</p></td>
<td><p>Load the California housing dataset (regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_species_distributions.html#sklearn.datasets.fetch_species_distributions" title="sklearn.datasets.fetch_species_distributions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fetch_species_distributions</span></code></a>(*[, data_home, ...])</p></td>
<td><p>Loader for species distribution dataset from Phillips et.</p></td>
</tr>
</tbody>
</table>
</div>
<section id="the-olivetti-faces-dataset">
<span id="olivetti-faces-dataset"></span><h2><span class="section-number">7.2.1. </span>The Olivetti faces dataset<a class="headerlink" href="#the-olivetti-faces-dataset" title="Link to this heading">#</a></h2>
<p><a class="reference external" href="https://cam-orl.co.uk/facedatabase.html">This dataset contains a set of face images</a> taken between April 1992 and
April 1994 at AT&T Laboratories Cambridge. The
<a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_olivetti_faces.html#sklearn.datasets.fetch_olivetti_faces" title="sklearn.datasets.fetch_olivetti_faces"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_olivetti_faces</span></code></a> function is the data
fetching / caching function that downloads the data
archive from AT&T.</p>
<p>As described on the original website:</p>
<blockquote>
<div><p>There are ten different images of each of 40 distinct subjects. For some
subjects, the images were taken at different times, varying the lighting,
facial expressions (open / closed eyes, smiling / not smiling) and facial
details (glasses / no glasses). All the images were taken against a dark
homogeneous background with the subjects in an upright, frontal position
(with tolerance for some side movement).</p>
</div></blockquote>
<p><strong>Data Set Characteristics:</strong></p>
<div class="pst-scrollable-table-container"><table class="table">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>40</p></td>
</tr>
<tr class="row-even"><td><p>Samples total</p></td>
<td><p>400</p></td>
</tr>
<tr class="row-odd"><td><p>Dimensionality</p></td>
<td><p>4096</p></td>
</tr>
<tr class="row-even"><td><p>Features</p></td>
<td><p>real, between 0 and 1</p></td>
</tr>
</tbody>
</table>
</div>
<p>The image is quantized to 256 grey levels and stored as unsigned 8-bit
integers; the loader will convert these to floating point values on the
interval [0, 1], which are easier to work with for many algorithms.</p>
<p>The “target” for this database is an integer from 0 to 39 indicating the
identity of the person pictured; however, with only 10 examples per class, this
relatively small dataset is more interesting from an unsupervised or
semi-supervised perspective.</p>
<p>The original dataset consisted of 92 x 112, while the version available here
consists of 64x64 images.</p>
<p>When using these images, please give credit to AT&T Laboratories Cambridge.</p>
</section>
<section id="the-20-newsgroups-text-dataset">
<span id="newsgroups-dataset"></span><h2><span class="section-number">7.2.2. </span>The 20 newsgroups text dataset<a class="headerlink" href="#the-20-newsgroups-text-dataset" title="Link to this heading">#</a></h2>
<p>The 20 newsgroups dataset comprises around 18000 newsgroups posts on
20 topics split in two subsets: one for training (or development)
and the other one for testing (or for performance evaluation). The split
between the train and test set is based upon a messages posted before
and after a specific date.</p>
<p>This module contains two loaders. The first one,
<a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_20newsgroups</span></code></a>,
returns a list of the raw texts that can be fed to text feature
extractors such as <a class="reference internal" href="../modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a>
with custom parameters so as to extract feature vectors.
The second one, <a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_20newsgroups_vectorized.html#sklearn.datasets.fetch_20newsgroups_vectorized" title="sklearn.datasets.fetch_20newsgroups_vectorized"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_20newsgroups_vectorized</span></code></a>,
returns ready-to-use features, i.e., it is not necessary to use a feature
extractor.</p>
<p><strong>Data Set Characteristics:</strong></p>
<div class="pst-scrollable-table-container"><table class="table">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>20</p></td>
</tr>
<tr class="row-even"><td><p>Samples total</p></td>
<td><p>18846</p></td>
</tr>
<tr class="row-odd"><td><p>Dimensionality</p></td>
<td><p>1</p></td>
</tr>
<tr class="row-even"><td><p>Features</p></td>
<td><p>text</p></td>
</tr>
</tbody>
</table>
</div>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="usage">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Usage<a class="headerlink" href="#usage" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">The <a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_20newsgroups</span></code></a> function is a data
fetching / caching functions that downloads the data archive from
the original <a class="reference external" href="http://people.csail.mit.edu/jrennie/20Newsgroups/">20 newsgroups website</a>,
extracts the archive contents
in the <code class="docutils literal notranslate"><span class="pre">~/scikit_learn_data/20news_home</span></code> folder and calls the
<a class="reference internal" href="../modules/generated/sklearn.datasets.load_files.html#sklearn.datasets.load_files" title="sklearn.datasets.load_files"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.load_files</span></code></a> on either the training or
testing set folder, or both of them:</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">fetch_20newsgroups</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'train'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
<span class="gp">>>> </span><span class="n">pprint</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target_names</span><span class="p">))</span>
<span class="go">['alt.atheism',</span>
<span class="go"> 'comp.graphics',</span>
<span class="go"> 'comp.os.ms-windows.misc',</span>
<span class="go"> 'comp.sys.ibm.pc.hardware',</span>
<span class="go"> 'comp.sys.mac.hardware',</span>
<span class="go"> 'comp.windows.x',</span>
<span class="go"> 'misc.forsale',</span>
<span class="go"> 'rec.autos',</span>
<span class="go"> 'rec.motorcycles',</span>
<span class="go"> 'rec.sport.baseball',</span>
<span class="go"> 'rec.sport.hockey',</span>
<span class="go"> 'sci.crypt',</span>
<span class="go"> 'sci.electronics',</span>
<span class="go"> 'sci.med',</span>
<span class="go"> 'sci.space',</span>
<span class="go"> 'soc.religion.christian',</span>
<span class="go"> 'talk.politics.guns',</span>
<span class="go"> 'talk.politics.mideast',</span>
<span class="go"> 'talk.politics.misc',</span>
<span class="go"> 'talk.religion.misc']</span>
</pre></div>
</div>
<p class="sd-card-text">The real data lies in the <code class="docutils literal notranslate"><span class="pre">filenames</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code> attributes. The target
attribute is the integer index of the category:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">filenames</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(11314,)</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(11314,)</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
<span class="go">array([ 7, 4, 4, 1, 14, 16, 13, 3, 2, 4])</span>
</pre></div>
</div>
<p class="sd-card-text">It is possible to load only a sub-selection of the categories by passing the
list of the categories to load to the
<a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups" title="sklearn.datasets.fetch_20newsgroups"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_20newsgroups</span></code></a> function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">cats</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'alt.atheism'</span><span class="p">,</span> <span class="s1">'sci.space'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'train'</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</span><span class="n">cats</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="go">['alt.atheism', 'sci.space']</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">filenames</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(1073,)</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(1073,)</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
<span class="go">array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])</span>
</pre></div>
</div>
</div>
</details><details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="converting-text-to-vectors">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Converting text to vectors<a class="headerlink" href="#converting-text-to-vectors" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">In order to feed predictive or clustering models with the text data,
one first need to turn the text into vectors of numerical values suitable
for statistical analysis. This can be achieved with the utilities of the
<code class="docutils literal notranslate"><span class="pre">sklearn.feature_extraction.text</span></code> as demonstrated in the following
example that extract <a class="reference external" href="https://en.wikipedia.org/wiki/Tf-idf">TF-IDF</a> vectors
of unigram tokens from a subset of 20news:</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.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfVectorizer</span>
<span class="gp">>>> </span><span class="n">categories</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'alt.atheism'</span><span class="p">,</span> <span class="s1">'talk.religion.misc'</span><span class="p">,</span>
<span class="gp">... </span> <span class="s1">'comp.graphics'</span><span class="p">,</span> <span class="s1">'sci.space'</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">newsgroups_train</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'train'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">vectors</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">newsgroups_train</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">vectors</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(2034, 34118)</span>
</pre></div>
</div>
<p class="sd-card-text">The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero
components by sample in a more than 30000-dimensional space
(less than .5% non-zero features):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">vectors</span><span class="o">.</span><span class="n">nnz</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">vectors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="go">159.01327...</span>
</pre></div>
</div>
<p class="sd-card-text"><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_20newsgroups_vectorized.html#sklearn.datasets.fetch_20newsgroups_vectorized" title="sklearn.datasets.fetch_20newsgroups_vectorized"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_20newsgroups_vectorized</span></code></a> is a function which
returns ready-to-use token counts features instead of file names.</p>
</div>
</details><details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="filtering-text-for-more-realistic-training">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Filtering text for more realistic training<a class="headerlink" href="#filtering-text-for-more-realistic-training" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">It is easy for a classifier to overfit on particular things that appear in the
20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very
high F-scores, but their results would not generalize to other documents that
aren’t from this window of time.</p>
<p class="sd-card-text">For example, let’s look at the results of a multinomial Naive Bayes classifier,
which is fast to train and achieves a decent F-score:</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.naive_bayes</span> <span class="kn">import</span> <span class="n">MultinomialNB</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="gp">>>> </span><span class="n">newsgroups_test</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'test'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">vectors_test</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">newsgroups_test</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">MultinomialNB</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.01</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">vectors</span><span class="p">,</span> <span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="go">MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)</span>
<span class="gp">>>> </span><span class="n">pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">vectors_test</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">f1_score</span><span class="p">(</span><span class="n">newsgroups_test</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">'macro'</span><span class="p">)</span>
<span class="go">0.88213...</span>
</pre></div>
</div>
<p class="sd-card-text">(The example <a class="reference internal" href="../auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py"><span class="std std-ref">Classification of text documents using sparse features</span></a> shuffles
the training and test data, instead of segmenting by time, and in that case
multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious
yet of what’s going on inside this classifier?)</p>
<p class="sd-card-text">Let’s take a look at what the most informative features are:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">show_top10</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">categories</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">feature_names</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">()</span>
<span class="gp">... </span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">category</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">categories</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">top10</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">classifier</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="o">-</span><span class="mi">10</span><span class="p">:]</span>
<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%s</span><span class="s2">: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">category</span><span class="p">,</span> <span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">feature_names</span><span class="p">[</span><span class="n">top10</span><span class="p">])))</span>
<span class="gp">...</span>
<span class="gp">>>> </span><span class="n">show_top10</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">newsgroups_train</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="go">alt.atheism: edu it and in you that is of to the</span>
<span class="go">comp.graphics: edu in graphics it is for and of to the</span>
<span class="go">sci.space: edu it that is in and space to of the</span>
<span class="go">talk.religion.misc: not it you in is that and to of the</span>
</pre></div>
</div>
<p class="sd-card-text">You can now see many things that these features have overfit to:</p>
<ul class="simple">
<li><p class="sd-card-text">Almost every group is distinguished by whether headers such as
<code class="docutils literal notranslate"><span class="pre">NNTP-Posting-Host:</span></code> and <code class="docutils literal notranslate"><span class="pre">Distribution:</span></code> appear more or less often.</p></li>
<li><p class="sd-card-text">Another significant feature involves whether the sender is affiliated with
a university, as indicated either by their headers or their signature.</p></li>
<li><p class="sd-card-text">The word “article” is a significant feature, based on how often people quote
previous posts like this: “In article [article ID], [name] <[e-mail address]>
wrote:”</p></li>
<li><p class="sd-card-text">Other features match the names and e-mail addresses of particular people who
were posting at the time.</p></li>
</ul>
<p class="sd-card-text">With such an abundance of clues that distinguish newsgroups, the classifiers
barely have to identify topics from text at all, and they all perform at the
same high level.</p>
<p class="sd-card-text">For this reason, the functions that load 20 Newsgroups data provide a
parameter called <strong>remove</strong>, telling it what kinds of information to strip out
of each file. <strong>remove</strong> should be a tuple containing any subset of
<code class="docutils literal notranslate"><span class="pre">('headers',</span> <span class="pre">'footers',</span> <span class="pre">'quotes')</span></code>, telling it to remove headers, signature
blocks, and quotation blocks respectively.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">newsgroups_test</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">'test'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">remove</span><span class="o">=</span><span class="p">(</span><span class="s1">'headers'</span><span class="p">,</span> <span class="s1">'footers'</span><span class="p">,</span> <span class="s1">'quotes'</span><span class="p">),</span>
<span class="gp">... </span> <span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">vectors_test</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">newsgroups_test</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">vectors_test</span><span class="p">)</span>