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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="current reference internal" href="#">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>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<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="dataset-loading-utilities">
<span id="datasets"></span><h1><span class="section-number">7. </span>Dataset loading utilities<a class="headerlink" href="#dataset-loading-utilities" title="Link to this heading">#</a></h1>
<p>The <code class="docutils literal notranslate"><span class="pre">sklearn.datasets</span></code> package embeds some small toy datasets and provides helpers
to fetch larger datasets commonly used by the machine learning community to benchmark
algorithms on data that comes from the ‘real world’.</p>
<p>To evaluate the impact of the scale of the dataset (<code class="docutils literal notranslate"><span class="pre">n_samples</span></code> and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code>) while controlling the statistical properties of the data
(typically the correlation and informativeness of the features), it is
also possible to generate synthetic data.</p>
<p><strong>General dataset API.</strong> There are three main kinds of dataset interfaces that
can be used to get datasets depending on the desired type of dataset.</p>
<p><strong>The dataset loaders.</strong> They can be used to load small standard datasets,
described in the <a class="reference internal" href="datasets/toy_dataset.html#toy-datasets"><span class="std std-ref">Toy datasets</span></a> section.</p>
<p><strong>The dataset fetchers.</strong> They can be used to download and load larger datasets,
described in the <a class="reference internal" href="datasets/real_world.html#real-world-datasets"><span class="std std-ref">Real world datasets</span></a> section.</p>
<p>Both loaders and fetchers functions return a <a class="reference internal" href="modules/generated/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>
object holding at least two items:
an array of shape <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> * <code class="docutils literal notranslate"><span class="pre">n_features</span></code> with
key <code class="docutils literal notranslate"><span class="pre">data</span></code> (except for 20newsgroups) and a numpy array of
length <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>, containing the target values, with key <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
<p>The Bunch object is a dictionary that exposes its keys as attributes.
For more information about Bunch object, see <a class="reference internal" href="modules/generated/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>.</p>
<p>It’s also possible for almost all of these function to constrain the output
to be a tuple containing only the data and the target, by setting the
<code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
<p>The datasets also contain a full description in their <code class="docutils literal notranslate"><span class="pre">DESCR</span></code> attribute and
some contain <code class="docutils literal notranslate"><span class="pre">feature_names</span></code> and <code class="docutils literal notranslate"><span class="pre">target_names</span></code>. See the dataset
descriptions below for details.</p>
<p><strong>The dataset generation functions.</strong> They can be used to generate controlled
synthetic datasets, described in the <a class="reference internal" href="datasets/sample_generators.html#sample-generators"><span class="std std-ref">Generated datasets</span></a> section.</p>
<p>These functions return a tuple <code class="docutils literal notranslate"><span class="pre">(X,</span> <span class="pre">y)</span></code> consisting of a <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> *
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> numpy array <code class="docutils literal notranslate"><span class="pre">X</span></code> and an array of length <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>
containing the targets <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
<p>In addition, there are also miscellaneous tools to load datasets of other
formats or from other locations, described in the <a class="reference internal" href="datasets/loading_other_datasets.html#loading-other-datasets"><span class="std std-ref">Loading other datasets</span></a>
section.</p>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#iris-plants-dataset">7.1.1. Iris plants dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#diabetes-dataset">7.1.2. Diabetes dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#optical-recognition-of-handwritten-digits-dataset">7.1.3. Optical recognition of handwritten digits dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#linnerrud-dataset">7.1.4. Linnerrud dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#wine-recognition-dataset">7.1.5. Wine recognition dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html#breast-cancer-wisconsin-diagnostic-dataset">7.1.6. Breast cancer wisconsin (diagnostic) dataset</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#the-olivetti-faces-dataset">7.2.1. The Olivetti faces dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#the-20-newsgroups-text-dataset">7.2.2. The 20 newsgroups text dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#the-labeled-faces-in-the-wild-face-recognition-dataset">7.2.3. The Labeled Faces in the Wild face recognition dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#forest-covertypes">7.2.4. Forest covertypes</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#rcv1-dataset">7.2.5. RCV1 dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#kddcup-99-dataset">7.2.6. Kddcup 99 dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#california-housing-dataset">7.2.7. California Housing dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html#species-distribution-dataset">7.2.8. Species distribution dataset</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-classification-and-clustering">7.3.1. Generators for classification and clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-regression">7.3.2. Generators for regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-manifold-learning">7.3.3. Generators for manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html#generators-for-decomposition">7.3.4. Generators for decomposition</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#sample-images">7.4.1. Sample images</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#datasets-in-svmlight-libsvm-format">7.4.2. Datasets in svmlight / libsvm format</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#downloading-datasets-from-the-openml-org-repository">7.4.3. Downloading datasets from the openml.org repository</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html#loading-from-external-datasets">7.4.4. Loading from external datasets</a></li>
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