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<ul>
<li><a class="reference internal" href="#">API Reference</a><ul>
<li><a class="reference internal" href="#module-sklearn.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code>: Base classes and utility functions</a><ul>
<li><a class="reference internal" href="#base-classes">Base classes</a></li>
<li><a class="reference internal" href="#functions">Functions</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.calibration"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code>: Probability Calibration</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>: Clustering</a><ul>
<li><a class="reference internal" href="#classes">Classes</a></li>
<li><a class="reference internal" href="#id1">Functions</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code>: Composite Estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code>: Covariance Estimators</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.cross_decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code>: Cross decomposition</a></li>
<li><a class="reference internal" href="#module-sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>: Datasets</a><ul>
<li><a class="reference internal" href="#loaders">Loaders</a></li>
<li><a class="reference internal" href="#samples-generator">Samples generator</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code>: Matrix Decomposition</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.discriminant_analysis"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis</span></code>: Discriminant Analysis</a></li>
<li><a class="reference internal" href="#module-sklearn.dummy"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.dummy</span></code>: Dummy estimators</a></li>
<li><a class="reference internal" href="#module-sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code>: Ensemble Methods</a></li>
<li><a class="reference internal" href="#module-sklearn.exceptions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.exceptions</span></code>: Exceptions and warnings</a></li>
<li><a class="reference internal" href="#module-sklearn.experimental"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.experimental</span></code>: Experimental</a></li>
<li><a class="reference internal" href="#module-sklearn.feature_extraction"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction</span></code>: Feature Extraction</a><ul>
<li><a class="reference internal" href="#module-sklearn.feature_extraction.image">From images</a></li>
<li><a class="reference internal" href="#module-sklearn.feature_extraction.text">From text</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code>: Feature Selection</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.gaussian_process"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.gaussian_process</span></code>: Gaussian Processes</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.impute"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.impute</span></code>: Impute</a></li>
<li><a class="reference internal" href="#module-sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code>: Inspection</a><ul>
<li><a class="reference internal" href="#plotting">Plotting</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.isotonic"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.isotonic</span></code>: Isotonic regression</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.kernel_approximation"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_approximation</span></code>: Kernel Approximation</a></li>
<li><a class="reference internal" href="#module-sklearn.kernel_ridge"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.kernel_ridge</span></code>: Kernel Ridge Regression</a></li>
<li><a class="reference internal" href="#module-sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code>: Linear Models</a><ul>
<li><a class="reference internal" href="#linear-classifiers">Linear classifiers</a></li>
<li><a class="reference internal" href="#classical-linear-regressors">Classical linear regressors</a></li>
<li><a class="reference internal" href="#regressors-with-variable-selection">Regressors with variable selection</a></li>
<li><a class="reference internal" href="#bayesian-regressors">Bayesian regressors</a></li>
<li><a class="reference internal" href="#multi-task-linear-regressors-with-variable-selection">Multi-task linear regressors with variable selection</a></li>
<li><a class="reference internal" href="#outlier-robust-regressors">Outlier-robust regressors</a></li>
<li><a class="reference internal" href="#generalized-linear-models-glm-for-regression">Generalized linear models (GLM) for regression</a></li>
<li><a class="reference internal" href="#miscellaneous">Miscellaneous</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code>: Manifold Learning</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#sklearn-metrics-metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code>: Metrics</a><ul>
<li><a class="reference internal" href="#model-selection-interface">Model Selection Interface</a></li>
<li><a class="reference internal" href="#classification-metrics">Classification metrics</a></li>
<li><a class="reference internal" href="#regression-metrics">Regression metrics</a></li>
<li><a class="reference internal" href="#multilabel-ranking-metrics">Multilabel ranking metrics</a></li>
<li><a class="reference internal" href="#clustering-metrics">Clustering metrics</a></li>
<li><a class="reference internal" href="#biclustering-metrics">Biclustering metrics</a></li>
<li><a class="reference internal" href="#distance-metrics">Distance metrics</a></li>
<li><a class="reference internal" href="#pairwise-metrics">Pairwise metrics</a></li>
<li><a class="reference internal" href="#id7">Plotting</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.mixture"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.mixture</span></code>: Gaussian Mixture Models</a></li>
<li><a class="reference internal" href="#module-sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code>: Model Selection</a><ul>
<li><a class="reference internal" href="#splitter-classes">Splitter Classes</a></li>
<li><a class="reference internal" href="#splitter-functions">Splitter Functions</a></li>
<li><a class="reference internal" href="#hyper-parameter-optimizers">Hyper-parameter optimizers</a></li>
<li><a class="reference internal" href="#model-validation">Model validation</a></li>
<li><a class="reference internal" href="#visualization">Visualization</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.multiclass"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multiclass</span></code>: Multiclass classification</a><ul>
<li><a class="reference internal" href="#multiclass-classification-strategies">Multiclass classification strategies</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.multioutput"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.multioutput</span></code>: Multioutput regression and classification</a></li>
<li><a class="reference internal" href="#module-sklearn.naive_bayes"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.naive_bayes</span></code>: Naive Bayes</a></li>
<li><a class="reference internal" href="#module-sklearn.neighbors"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neighbors</span></code>: Nearest Neighbors</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.neural_network"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.neural_network</span></code>: Neural network models</a></li>
<li><a class="reference internal" href="#module-sklearn.pipeline"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.pipeline</span></code>: Pipeline</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code>: Preprocessing and Normalization</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.random_projection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.random_projection</span></code>: Random projection</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.semi_supervised"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.semi_supervised</span></code>: Semi-Supervised Learning</a></li>
<li><a class="reference internal" href="#module-sklearn.svm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.svm</span></code>: Support Vector Machines</a><ul>
<li><a class="reference internal" href="#estimators">Estimators</a><ul>
</ul>
</li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.tree"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.tree</span></code>: Decision Trees</a><ul>
<li><a class="reference internal" href="#id9">Plotting</a></li>
</ul>
</li>
<li><a class="reference internal" href="#module-sklearn.utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.utils</span></code>: Utilities</a><ul>
</ul>
</li>
<li><a class="reference internal" href="#recently-deprecated">Recently deprecated</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div>
<div id="sk-page-content-wrapper">
<div class="sk-page-content container-fluid body px-md-3" role="main">
<section id="api-reference">
<span id="api-ref"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h1>
<p>This is the class and function reference of scikit-learn. Please refer to
the <a class="reference internal" href="../user_guide.html#user-guide"><span class="std std-ref">full user guide</span></a> for further details, as the class and
function raw specifications may not be enough to give full guidelines on their
uses.
For reference on concepts repeated across the API, see <a class="reference internal" href="../glossary.html#glossary"><span class="std std-ref">Glossary of Common Terms and API Elements</span></a>.</p>
<section id="module-sklearn.base">
<span id="sklearn-base-base-classes-and-utility-functions"></span><h2><a class="reference internal" href="#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code></a>: Base classes and utility functions<a class="headerlink" href="#module-sklearn.base" title="Permalink to this heading">¶</a></h2>
<p>Base classes for all estimators.</p>
<section id="base-classes">
<h3>Base classes<a class="headerlink" href="#base-classes" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BaseEstimator</span></code></a></p></td>
<td><p>Base class for all estimators in scikit-learn.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.BiclusterMixin.html#sklearn.base.BiclusterMixin" title="sklearn.base.BiclusterMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.BiclusterMixin</span></code></a></p></td>
<td><p>Mixin class for all bicluster estimators in scikit-learn.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.ClassifierMixin</span></code></a></p></td>
<td><p>Mixin class for all classifiers in scikit-learn.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.ClusterMixin.html#sklearn.base.ClusterMixin" title="sklearn.base.ClusterMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.ClusterMixin</span></code></a></p></td>
<td><p>Mixin class for all cluster estimators in scikit-learn.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.DensityMixin.html#sklearn.base.DensityMixin" title="sklearn.base.DensityMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.DensityMixin</span></code></a></p></td>
<td><p>Mixin class for all density estimators in scikit-learn.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.RegressorMixin.html#sklearn.base.RegressorMixin" title="sklearn.base.RegressorMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.RegressorMixin</span></code></a></p></td>
<td><p>Mixin class for all regression estimators in scikit-learn.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.TransformerMixin.html#sklearn.base.TransformerMixin" title="sklearn.base.TransformerMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.TransformerMixin</span></code></a></p></td>
<td><p>Mixin class for all transformers in scikit-learn.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.MetaEstimatorMixin.html#sklearn.base.MetaEstimatorMixin" title="sklearn.base.MetaEstimatorMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.MetaEstimatorMixin</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.OneToOneFeatureMixin.html#sklearn.base.OneToOneFeatureMixin" title="sklearn.base.OneToOneFeatureMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.OneToOneFeatureMixin</span></code></a></p></td>
<td><p>Provides <code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code> for simple transformers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.OutlierMixin.html#sklearn.base.OutlierMixin" title="sklearn.base.OutlierMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.OutlierMixin</span></code></a></p></td>
<td><p>Mixin class for all outlier detection estimators in scikit-learn.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.ClassNamePrefixFeaturesOutMixin.html#sklearn.base.ClassNamePrefixFeaturesOutMixin" title="sklearn.base.ClassNamePrefixFeaturesOutMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.ClassNamePrefixFeaturesOutMixin</span></code></a></p></td>
<td><p>Mixin class for transformers that generate their own names by prefixing.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.feature_selection.SelectorMixin.html#sklearn.feature_selection.SelectorMixin" title="sklearn.feature_selection.SelectorMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_selection.SelectorMixin</span></code></a></p></td>
<td><p>Transformer mixin that performs feature selection given a support mask</p></td>
</tr>
</tbody>
</table>
</section>
<section id="functions">
<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.clone.html#sklearn.base.clone" title="sklearn.base.clone"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.clone</span></code></a>(estimator, *[, safe])</p></td>
<td><p>Construct a new unfitted estimator with the same parameters.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.base.is_classifier.html#sklearn.base.is_classifier" title="sklearn.base.is_classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.is_classifier</span></code></a>(estimator)</p></td>
<td><p>Return True if the given estimator is (probably) a classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.base.is_regressor.html#sklearn.base.is_regressor" title="sklearn.base.is_regressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">base.is_regressor</span></code></a>(estimator)</p></td>
<td><p>Return True if the given estimator is (probably) a regressor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.config_context.html#sklearn.config_context" title="sklearn.config_context"><code class="xref py py-obj docutils literal notranslate"><span class="pre">config_context</span></code></a>(*[, assume_finite, ...])</p></td>
<td><p>Context manager for global scikit-learn configuration.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.get_config.html#sklearn.get_config" title="sklearn.get_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_config</span></code></a>()</p></td>
<td><p>Retrieve current values for configuration set by <a class="reference internal" href="generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_config</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_config</span></code></a>([assume_finite, working_memory, ...])</p></td>
<td><p>Set global scikit-learn configuration.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.show_versions.html#sklearn.show_versions" title="sklearn.show_versions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show_versions</span></code></a>()</p></td>
<td><p>Print useful debugging information"</p></td>
</tr>
</tbody>
</table>
</section>
</section>
<section id="module-sklearn.calibration">
<span id="sklearn-calibration-probability-calibration"></span><span id="calibration-ref"></span><h2><a class="reference internal" href="#module-sklearn.calibration" title="sklearn.calibration"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code></a>: Probability Calibration<a class="headerlink" href="#module-sklearn.calibration" title="Permalink to this heading">¶</a></h2>
<p>Calibration of predicted probabilities.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="calibration.html#calibration"><span class="std std-ref">Probability calibration</span></a> section for further details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calibration.CalibratedClassifierCV</span></code></a>([...])</p></td>
<td><p>Probability calibration with isotonic regression or logistic regression.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.calibration.calibration_curve.html#sklearn.calibration.calibration_curve" title="sklearn.calibration.calibration_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calibration.calibration_curve</span></code></a>(y_true, y_prob, *)</p></td>
<td><p>Compute true and predicted probabilities for a calibration curve.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.cluster">
<span id="sklearn-cluster-clustering"></span><span id="cluster-ref"></span><h2><a class="reference internal" href="#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>: Clustering<a class="headerlink" href="#module-sklearn.cluster" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a> module gathers popular unsupervised clustering
algorithms.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="clustering.html#clustering"><span class="std std-ref">Clustering</span></a> and <a class="reference internal" href="biclustering.html#biclustering"><span class="std std-ref">Biclustering</span></a> sections for
further details.</p>
<section id="classes">
<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation" title="sklearn.cluster.AffinityPropagation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.AffinityPropagation</span></code></a>(*[, damping, ...])</p></td>
<td><p>Perform Affinity Propagation Clustering of data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering" title="sklearn.cluster.AgglomerativeClustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.AgglomerativeClustering</span></code></a>([...])</p></td>
<td><p>Agglomerative Clustering.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.Birch.html#sklearn.cluster.Birch" title="sklearn.cluster.Birch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.Birch</span></code></a>(*[, threshold, ...])</p></td>
<td><p>Implements the BIRCH clustering algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN" title="sklearn.cluster.DBSCAN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.DBSCAN</span></code></a>([eps, min_samples, metric, ...])</p></td>
<td><p>Perform DBSCAN clustering from vector array or distance matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.HDBSCAN.html#sklearn.cluster.HDBSCAN" title="sklearn.cluster.HDBSCAN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.HDBSCAN</span></code></a>([min_cluster_size, ...])</p></td>
<td><p>Cluster data using hierarchical density-based clustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.FeatureAgglomeration.html#sklearn.cluster.FeatureAgglomeration" title="sklearn.cluster.FeatureAgglomeration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.FeatureAgglomeration</span></code></a>([n_clusters, ...])</p></td>
<td><p>Agglomerate features.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.KMeans</span></code></a>([n_clusters, init, n_init, ...])</p></td>
<td><p>K-Means clustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.BisectingKMeans.html#sklearn.cluster.BisectingKMeans" title="sklearn.cluster.BisectingKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.BisectingKMeans</span></code></a>([n_clusters, init, ...])</p></td>
<td><p>Bisecting K-Means clustering.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans" title="sklearn.cluster.MiniBatchKMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.MiniBatchKMeans</span></code></a>([n_clusters, init, ...])</p></td>
<td><p>Mini-Batch K-Means clustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.MeanShift.html#sklearn.cluster.MeanShift" title="sklearn.cluster.MeanShift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.MeanShift</span></code></a>(*[, bandwidth, seeds, ...])</p></td>
<td><p>Mean shift clustering using a flat kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.OPTICS.html#sklearn.cluster.OPTICS" title="sklearn.cluster.OPTICS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.OPTICS</span></code></a>(*[, min_samples, max_eps, ...])</p></td>
<td><p>Estimate clustering structure from vector array.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering" title="sklearn.cluster.SpectralClustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.SpectralClustering</span></code></a>([n_clusters, ...])</p></td>
<td><p>Apply clustering to a projection of the normalized Laplacian.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.SpectralBiclustering.html#sklearn.cluster.SpectralBiclustering" title="sklearn.cluster.SpectralBiclustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.SpectralBiclustering</span></code></a>([n_clusters, ...])</p></td>
<td><p>Spectral biclustering (Kluger, 2003).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.SpectralCoclustering.html#sklearn.cluster.SpectralCoclustering" title="sklearn.cluster.SpectralCoclustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.SpectralCoclustering</span></code></a>([n_clusters, ...])</p></td>
<td><p>Spectral Co-Clustering algorithm (Dhillon, 2001).</p></td>
</tr>
</tbody>
</table>
</section>
<section id="id1">
<h3>Functions<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.affinity_propagation.html#sklearn.cluster.affinity_propagation" title="sklearn.cluster.affinity_propagation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.affinity_propagation</span></code></a>(S, *[, ...])</p></td>
<td><p>Perform Affinity Propagation Clustering of data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.cluster_optics_dbscan.html#sklearn.cluster.cluster_optics_dbscan" title="sklearn.cluster.cluster_optics_dbscan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.cluster_optics_dbscan</span></code></a>(*, ...)</p></td>
<td><p>Perform DBSCAN extraction for an arbitrary epsilon.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.cluster_optics_xi.html#sklearn.cluster.cluster_optics_xi" title="sklearn.cluster.cluster_optics_xi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.cluster_optics_xi</span></code></a>(*, reachability, ...)</p></td>
<td><p>Automatically extract clusters according to the Xi-steep method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.compute_optics_graph.html#sklearn.cluster.compute_optics_graph" title="sklearn.cluster.compute_optics_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.compute_optics_graph</span></code></a>(X, *, ...)</p></td>
<td><p>Compute the OPTICS reachability graph.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/dbscan-function.html#sklearn.cluster.dbscan" title="sklearn.cluster.dbscan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.dbscan</span></code></a>(X[, eps, min_samples, ...])</p></td>
<td><p>Perform DBSCAN clustering from vector array or distance matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.estimate_bandwidth.html#sklearn.cluster.estimate_bandwidth" title="sklearn.cluster.estimate_bandwidth"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.estimate_bandwidth</span></code></a>(X, *[, quantile, ...])</p></td>
<td><p>Estimate the bandwidth to use with the mean-shift algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.k_means.html#sklearn.cluster.k_means" title="sklearn.cluster.k_means"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.k_means</span></code></a>(X, n_clusters, *[, ...])</p></td>
<td><p>Perform K-means clustering algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.kmeans_plusplus.html#sklearn.cluster.kmeans_plusplus" title="sklearn.cluster.kmeans_plusplus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.kmeans_plusplus</span></code></a>(X, n_clusters, *[, ...])</p></td>
<td><p>Init n_clusters seeds according to k-means++.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.mean_shift.html#sklearn.cluster.mean_shift" title="sklearn.cluster.mean_shift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.mean_shift</span></code></a>(X, *[, bandwidth, seeds, ...])</p></td>
<td><p>Perform mean shift clustering of data using a flat kernel.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cluster.spectral_clustering.html#sklearn.cluster.spectral_clustering" title="sklearn.cluster.spectral_clustering"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.spectral_clustering</span></code></a>(affinity, *[, ...])</p></td>
<td><p>Apply clustering to a projection of the normalized Laplacian.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cluster.ward_tree.html#sklearn.cluster.ward_tree" title="sklearn.cluster.ward_tree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.ward_tree</span></code></a>(X, *[, connectivity, ...])</p></td>
<td><p>Ward clustering based on a Feature matrix.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
<section id="module-sklearn.compose">
<span id="sklearn-compose-composite-estimators"></span><span id="compose-ref"></span><h2><a class="reference internal" href="#module-sklearn.compose" title="sklearn.compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code></a>: Composite Estimators<a class="headerlink" href="#module-sklearn.compose" title="Permalink to this heading">¶</a></h2>
<p>Meta-estimators for building composite models with transformers</p>
<p>In addition to its current contents, this module will eventually be home to
refurbished versions of Pipeline and FeatureUnion.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="compose.html#combining-estimators"><span class="std std-ref">Pipelines and composite estimators</span></a> section for further
details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.ColumnTransformer</span></code></a>(transformers, *[, ...])</p></td>
<td><p>Applies transformers to columns of an array or pandas DataFrame.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.compose.TransformedTargetRegressor.html#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose.TransformedTargetRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.TransformedTargetRegressor</span></code></a>([...])</p></td>
<td><p>Meta-estimator to regress on a transformed target.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.compose.make_column_transformer.html#sklearn.compose.make_column_transformer" title="sklearn.compose.make_column_transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.make_column_transformer</span></code></a>(*transformers)</p></td>
<td><p>Construct a ColumnTransformer from the given transformers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.compose.make_column_selector.html#sklearn.compose.make_column_selector" title="sklearn.compose.make_column_selector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compose.make_column_selector</span></code></a>([pattern, ...])</p></td>
<td><p>Create a callable to select columns to be used with <code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnTransformer</span></code>.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.covariance">
<span id="sklearn-covariance-covariance-estimators"></span><span id="covariance-ref"></span><h2><a class="reference internal" href="#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a>: Covariance Estimators<a class="headerlink" href="#module-sklearn.covariance" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a> module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="covariance.html#covariance"><span class="std std-ref">Covariance estimation</span></a> section for further details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.EmpiricalCovariance.html#sklearn.covariance.EmpiricalCovariance" title="sklearn.covariance.EmpiricalCovariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.EmpiricalCovariance</span></code></a>(*[, ...])</p></td>
<td><p>Maximum likelihood covariance estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope" title="sklearn.covariance.EllipticEnvelope"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.EllipticEnvelope</span></code></a>(*[, ...])</p></td>
<td><p>An object for detecting outliers in a Gaussian distributed dataset.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.GraphicalLasso.html#sklearn.covariance.GraphicalLasso" title="sklearn.covariance.GraphicalLasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.GraphicalLasso</span></code></a>([alpha, mode, ...])</p></td>
<td><p>Sparse inverse covariance estimation with an l1-penalized estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.GraphicalLassoCV.html#sklearn.covariance.GraphicalLassoCV" title="sklearn.covariance.GraphicalLassoCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.GraphicalLassoCV</span></code></a>(*[, alphas, ...])</p></td>
<td><p>Sparse inverse covariance w/ cross-validated choice of the l1 penalty.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.LedoitWolf.html#sklearn.covariance.LedoitWolf" title="sklearn.covariance.LedoitWolf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.LedoitWolf</span></code></a>(*[, store_precision, ...])</p></td>
<td><p>LedoitWolf Estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.MinCovDet.html#sklearn.covariance.MinCovDet" title="sklearn.covariance.MinCovDet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.MinCovDet</span></code></a>(*[, store_precision, ...])</p></td>
<td><p>Minimum Covariance Determinant (MCD): robust estimator of covariance.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.OAS.html#sklearn.covariance.OAS" title="sklearn.covariance.OAS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.OAS</span></code></a>(*[, store_precision, ...])</p></td>
<td><p>Oracle Approximating Shrinkage Estimator as proposed in <a class="reference internal" href="generated/sklearn.covariance.OAS.html#r69773891e6a6-1" id="id2"><span>[R69773891e6a6-1]</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.ShrunkCovariance.html#sklearn.covariance.ShrunkCovariance" title="sklearn.covariance.ShrunkCovariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.ShrunkCovariance</span></code></a>(*[, ...])</p></td>
<td><p>Covariance estimator with shrinkage.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.empirical_covariance.html#sklearn.covariance.empirical_covariance" title="sklearn.covariance.empirical_covariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.empirical_covariance</span></code></a>(X, *[, ...])</p></td>
<td><p>Compute the Maximum likelihood covariance estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.graphical_lasso.html#sklearn.covariance.graphical_lasso" title="sklearn.covariance.graphical_lasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.graphical_lasso</span></code></a>(emp_cov, alpha, *)</p></td>
<td><p>L1-penalized covariance estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.covariance.ledoit_wolf.html#sklearn.covariance.ledoit_wolf" title="sklearn.covariance.ledoit_wolf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.ledoit_wolf</span></code></a>(X, *[, ...])</p></td>
<td><p>Estimate the shrunk Ledoit-Wolf covariance matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.ledoit_wolf_shrinkage.html#sklearn.covariance.ledoit_wolf_shrinkage" title="sklearn.covariance.ledoit_wolf_shrinkage"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.ledoit_wolf_shrinkage</span></code></a>(X[, ...])</p></td>
<td><p>Estimate the shrunk Ledoit-Wolf covariance matrix.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/oas-function.html#sklearn.covariance.oas" title="sklearn.covariance.oas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.oas</span></code></a>(X, *[, assume_centered])</p></td>
<td><p>Estimate covariance with the Oracle Approximating Shrinkage as proposed in <a class="reference internal" href="generated/oas-function.html#rca3a42e5ec35-1" id="id3"><span>[Rca3a42e5ec35-1]</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.covariance.shrunk_covariance.html#sklearn.covariance.shrunk_covariance" title="sklearn.covariance.shrunk_covariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">covariance.shrunk_covariance</span></code></a>(emp_cov[, ...])</p></td>
<td><p>Calculate a covariance matrix shrunk on the diagonal.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.cross_decomposition">
<span id="sklearn-cross-decomposition-cross-decomposition"></span><span id="cross-decomposition-ref"></span><h2><a class="reference internal" href="#module-sklearn.cross_decomposition" title="sklearn.cross_decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code></a>: Cross decomposition<a class="headerlink" href="#module-sklearn.cross_decomposition" title="Permalink to this heading">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="cross_decomposition.html#cross-decomposition"><span class="std std-ref">Cross decomposition</span></a> section for further details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.CCA.html#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition.CCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.CCA</span></code></a>([n_components, ...])</p></td>
<td><p>Canonical Correlation Analysis, also known as "Mode B" PLS.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSCanonical.html#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition.PLSCanonical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.PLSCanonical</span></code></a>([...])</p></td>
<td><p>Partial Least Squares transformer and regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSRegression.html#sklearn.cross_decomposition.PLSRegression" title="sklearn.cross_decomposition.PLSRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.PLSRegression</span></code></a>([...])</p></td>
<td><p>PLS regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.cross_decomposition.PLSSVD.html#sklearn.cross_decomposition.PLSSVD" title="sklearn.cross_decomposition.PLSSVD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_decomposition.PLSSVD</span></code></a>([n_components, ...])</p></td>
<td><p>Partial Least Square SVD.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.datasets">
<span id="sklearn-datasets-datasets"></span><span id="datasets-ref"></span><h2><a class="reference internal" href="#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>: Datasets<a class="headerlink" href="#module-sklearn.datasets" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a> module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="../datasets.html#datasets"><span class="std std-ref">Dataset loading utilities</span></a> section for further details.</p>
<section id="loaders">
<h3>Loaders<a class="headerlink" href="#loaders" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.clear_data_home.html#sklearn.datasets.clear_data_home" title="sklearn.datasets.clear_data_home"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.clear_data_home</span></code></a>([data_home])</p></td>
<td><p>Delete all the content of the data home cache.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.dump_svmlight_file.html#sklearn.datasets.dump_svmlight_file" title="sklearn.datasets.dump_svmlight_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.dump_svmlight_file</span></code></a>(X, y, f, *[, ...])</p></td>
<td><p>Dump the dataset in svmlight / libsvm file format.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="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">datasets.fetch_20newsgroups</span></code></a>(*[, data_home, ...])</p></td>
<td><p>Load the filenames and data from the 20 newsgroups dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="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">datasets.fetch_20newsgroups_vectorized</span></code></a>(*[, ...])</p></td>
<td><p>Load and vectorize the 20 newsgroups dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="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">datasets.fetch_california_housing</span></code></a>(*[, ...])</p></td>
<td><p>Load the California housing dataset (regression).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="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">datasets.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="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">datasets.fetch_kddcup99</span></code></a>(*[, subset, ...])</p></td>
<td><p>Load the kddcup99 dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="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">datasets.fetch_lfw_pairs</span></code></a>(*[, subset, ...])</p></td>
<td><p>Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="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">datasets.fetch_lfw_people</span></code></a>(*[, data_home, ...])</p></td>
<td><p>Load the Labeled Faces in the Wild (LFW) people dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="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">datasets.fetch_olivetti_faces</span></code></a>(*[, ...])</p></td>
<td><p>Load the Olivetti faces data-set from AT&T (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml" title="sklearn.datasets.fetch_openml"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.fetch_openml</span></code></a>([name, version, ...])</p></td>
<td><p>Fetch dataset from openml by name or dataset id.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="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">datasets.fetch_rcv1</span></code></a>(*[, data_home, subset, ...])</p></td>
<td><p>Load the RCV1 multilabel dataset (classification).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="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">datasets.fetch_species_distributions</span></code></a>(*[, ...])</p></td>
<td><p>Loader for species distribution dataset from Phillips et.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.get_data_home.html#sklearn.datasets.get_data_home" title="sklearn.datasets.get_data_home"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.get_data_home</span></code></a>([data_home])</p></td>
<td><p>Return the path of the scikit-learn data directory.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer" title="sklearn.datasets.load_breast_cancer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_breast_cancer</span></code></a>(*[, return_X_y, ...])</p></td>
<td><p>Load and return the breast cancer wisconsin dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes" title="sklearn.datasets.load_diabetes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_diabetes</span></code></a>(*[, return_X_y, ...])</p></td>
<td><p>Load and return the diabetes dataset (regression).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_digits</span></code></a>(*[, n_class, ...])</p></td>
<td><p>Load and return the digits dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_files.html#sklearn.datasets.load_files" title="sklearn.datasets.load_files"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_files</span></code></a>(container_path, *[, ...])</p></td>
<td><p>Load text files with categories as subfolder names.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_iris</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the iris dataset (classification).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud" title="sklearn.datasets.load_linnerud"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_linnerud</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the physical exercise Linnerud dataset.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_sample_image.html#sklearn.datasets.load_sample_image" title="sklearn.datasets.load_sample_image"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_sample_image</span></code></a>(image_name)</p></td>
<td><p>Load the numpy array of a single sample image.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_sample_images.html#sklearn.datasets.load_sample_images" title="sklearn.datasets.load_sample_images"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_sample_images</span></code></a>()</p></td>
<td><p>Load sample images for image manipulation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_svmlight_file.html#sklearn.datasets.load_svmlight_file" title="sklearn.datasets.load_svmlight_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_svmlight_file</span></code></a>(f, *[, ...])</p></td>
<td><p>Load datasets in the svmlight / libsvm format into sparse CSR matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_svmlight_files.html#sklearn.datasets.load_svmlight_files" title="sklearn.datasets.load_svmlight_files"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_svmlight_files</span></code></a>(files, *[, ...])</p></td>
<td><p>Load dataset from multiple files in SVMlight format.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine" title="sklearn.datasets.load_wine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.load_wine</span></code></a>(*[, return_X_y, as_frame])</p></td>
<td><p>Load and return the wine dataset (classification).</p></td>
</tr>
</tbody>
</table>
</section>
<section id="samples-generator">
<h3>Samples generator<a class="headerlink" href="#samples-generator" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_biclusters.html#sklearn.datasets.make_biclusters" title="sklearn.datasets.make_biclusters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_biclusters</span></code></a>(shape, n_clusters, *)</p></td>
<td><p>Generate a constant block diagonal structure array for biclustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_blobs</span></code></a>([n_samples, n_features, ...])</p></td>
<td><p>Generate isotropic Gaussian blobs for clustering.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_checkerboard</span></code></a>(shape, n_clusters, *)</p></td>
<td><p>Generate an array with block checkerboard structure for biclustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_circles</span></code></a>([n_samples, shuffle, ...])</p></td>
<td><p>Make a large circle containing a smaller circle in 2d.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_classification</span></code></a>([n_samples, ...])</p></td>
<td><p>Generate a random n-class classification problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_friedman1.html#sklearn.datasets.make_friedman1" title="sklearn.datasets.make_friedman1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_friedman1</span></code></a>([n_samples, ...])</p></td>
<td><p>Generate the "Friedman #1" regression problem.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_friedman2.html#sklearn.datasets.make_friedman2" title="sklearn.datasets.make_friedman2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_friedman2</span></code></a>([n_samples, noise, ...])</p></td>
<td><p>Generate the "Friedman #2" regression problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_friedman3.html#sklearn.datasets.make_friedman3" title="sklearn.datasets.make_friedman3"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_friedman3</span></code></a>([n_samples, noise, ...])</p></td>
<td><p>Generate the "Friedman #3" regression problem.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles" title="sklearn.datasets.make_gaussian_quantiles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_gaussian_quantiles</span></code></a>(*[, mean, ...])</p></td>
<td><p>Generate isotropic Gaussian and label samples by quantile.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_hastie_10_2.html#sklearn.datasets.make_hastie_10_2" title="sklearn.datasets.make_hastie_10_2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_hastie_10_2</span></code></a>([n_samples, ...])</p></td>
<td><p>Generate data for binary classification used in Hastie et al. 2009, Example 10.2.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_low_rank_matrix</span></code></a>([n_samples, ...])</p></td>
<td><p>Generate a mostly low rank matrix with bell-shaped singular values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_moons</span></code></a>([n_samples, shuffle, ...])</p></td>
<td><p>Make two interleaving half circles.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_multilabel_classification.html#sklearn.datasets.make_multilabel_classification" title="sklearn.datasets.make_multilabel_classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_multilabel_classification</span></code></a>([...])</p></td>
<td><p>Generate a random multilabel classification problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_regression</span></code></a>([n_samples, ...])</p></td>
<td><p>Generate a random regression problem.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_s_curve.html#sklearn.datasets.make_s_curve" title="sklearn.datasets.make_s_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_s_curve</span></code></a>([n_samples, noise, ...])</p></td>
<td><p>Generate an S curve dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_sparse_coded_signal.html#sklearn.datasets.make_sparse_coded_signal" title="sklearn.datasets.make_sparse_coded_signal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_sparse_coded_signal</span></code></a>(n_samples, ...)</p></td>
<td><p>Generate a signal as a sparse combination of dictionary elements.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_sparse_spd_matrix.html#sklearn.datasets.make_sparse_spd_matrix" title="sklearn.datasets.make_sparse_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_sparse_spd_matrix</span></code></a>([dim, ...])</p></td>
<td><p>Generate a sparse symmetric definite positive matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_sparse_uncorrelated.html#sklearn.datasets.make_sparse_uncorrelated" title="sklearn.datasets.make_sparse_uncorrelated"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_sparse_uncorrelated</span></code></a>([...])</p></td>
<td><p>Generate a random regression problem with sparse uncorrelated design.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_spd_matrix.html#sklearn.datasets.make_spd_matrix" title="sklearn.datasets.make_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_spd_matrix</span></code></a>(n_dim, *[, ...])</p></td>
<td><p>Generate a random symmetric, positive-definite matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.datasets.make_swiss_roll.html#sklearn.datasets.make_swiss_roll" title="sklearn.datasets.make_swiss_roll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">datasets.make_swiss_roll</span></code></a>([n_samples, noise, ...])</p></td>
<td><p>Generate a swiss roll dataset.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
<section id="module-sklearn.decomposition">
<span id="sklearn-decomposition-matrix-decomposition"></span><span id="decomposition-ref"></span><h2><a class="reference internal" href="#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a>: Matrix Decomposition<a class="headerlink" href="#module-sklearn.decomposition" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a> module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="decomposition.html#decompositions"><span class="std std-ref">Decomposing signals in components (matrix factorization problems)</span></a> section for further details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.DictionaryLearning.html#sklearn.decomposition.DictionaryLearning" title="sklearn.decomposition.DictionaryLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.DictionaryLearning</span></code></a>([...])</p></td>
<td><p>Dictionary learning.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.FactorAnalysis.html#sklearn.decomposition.FactorAnalysis" title="sklearn.decomposition.FactorAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.FactorAnalysis</span></code></a>([n_components, ...])</p></td>
<td><p>Factor Analysis (FA).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.FastICA.html#sklearn.decomposition.FastICA" title="sklearn.decomposition.FastICA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.FastICA</span></code></a>([n_components, ...])</p></td>
<td><p>FastICA: a fast algorithm for Independent Component Analysis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.IncrementalPCA.html#sklearn.decomposition.IncrementalPCA" title="sklearn.decomposition.IncrementalPCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.IncrementalPCA</span></code></a>([n_components, ...])</p></td>
<td><p>Incremental principal components analysis (IPCA).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.KernelPCA.html#sklearn.decomposition.KernelPCA" title="sklearn.decomposition.KernelPCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.KernelPCA</span></code></a>([n_components, ...])</p></td>
<td><p>Kernel Principal component analysis (KPCA) <a class="reference internal" href="generated/sklearn.decomposition.KernelPCA.html#r396fc7d924b8-1" id="id4"><span>[R396fc7d924b8-1]</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.LatentDirichletAllocation.html#sklearn.decomposition.LatentDirichletAllocation" title="sklearn.decomposition.LatentDirichletAllocation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.LatentDirichletAllocation</span></code></a>([...])</p></td>
<td><p>Latent Dirichlet Allocation with online variational Bayes algorithm.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.MiniBatchDictionaryLearning.html#sklearn.decomposition.MiniBatchDictionaryLearning" title="sklearn.decomposition.MiniBatchDictionaryLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.MiniBatchDictionaryLearning</span></code></a>([...])</p></td>
<td><p>Mini-batch dictionary learning.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.MiniBatchSparsePCA.html#sklearn.decomposition.MiniBatchSparsePCA" title="sklearn.decomposition.MiniBatchSparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.MiniBatchSparsePCA</span></code></a>([...])</p></td>
<td><p>Mini-batch Sparse Principal Components Analysis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF" title="sklearn.decomposition.NMF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.NMF</span></code></a>([n_components, init, ...])</p></td>
<td><p>Non-Negative Matrix Factorization (NMF).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.MiniBatchNMF.html#sklearn.decomposition.MiniBatchNMF" title="sklearn.decomposition.MiniBatchNMF"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.MiniBatchNMF</span></code></a>([n_components, ...])</p></td>
<td><p>Mini-Batch Non-Negative Matrix Factorization (NMF).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a>([n_components, copy, ...])</p></td>
<td><p>Principal component analysis (PCA).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA" title="sklearn.decomposition.SparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.SparsePCA</span></code></a>([n_components, ...])</p></td>
<td><p>Sparse Principal Components Analysis (SparsePCA).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.SparseCoder.html#sklearn.decomposition.SparseCoder" title="sklearn.decomposition.SparseCoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.SparseCoder</span></code></a>(dictionary, *[, ...])</p></td>
<td><p>Sparse coding.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.TruncatedSVD.html#sklearn.decomposition.TruncatedSVD" title="sklearn.decomposition.TruncatedSVD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.TruncatedSVD</span></code></a>([n_components, ...])</p></td>
<td><p>Dimensionality reduction using truncated SVD (aka LSA).</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.dict_learning.html#sklearn.decomposition.dict_learning" title="sklearn.decomposition.dict_learning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.dict_learning</span></code></a>(X, n_components, ...)</p></td>
<td><p>Solve a dictionary learning matrix factorization problem.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.dict_learning_online.html#sklearn.decomposition.dict_learning_online" title="sklearn.decomposition.dict_learning_online"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.dict_learning_online</span></code></a>(X[, ...])</p></td>
<td><p>Solve a dictionary learning matrix factorization problem online.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/fastica-function.html#sklearn.decomposition.fastica" title="sklearn.decomposition.fastica"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.fastica</span></code></a>(X[, n_components, ...])</p></td>
<td><p>Perform Fast Independent Component Analysis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.decomposition.non_negative_factorization.html#sklearn.decomposition.non_negative_factorization" title="sklearn.decomposition.non_negative_factorization"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.non_negative_factorization</span></code></a>(X)</p></td>
<td><p>Compute Non-negative Matrix Factorization (NMF).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.decomposition.sparse_encode.html#sklearn.decomposition.sparse_encode" title="sklearn.decomposition.sparse_encode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decomposition.sparse_encode</span></code></a>(X, dictionary, *)</p></td>
<td><p>Sparse coding.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.discriminant_analysis">
<span id="sklearn-discriminant-analysis-discriminant-analysis"></span><span id="lda-ref"></span><h2><a class="reference internal" href="#module-sklearn.discriminant_analysis" title="sklearn.discriminant_analysis"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.discriminant_analysis</span></code></a>: Discriminant Analysis<a class="headerlink" href="#module-sklearn.discriminant_analysis" title="Permalink to this heading">¶</a></h2>
<p>Linear Discriminant Analysis and Quadratic Discriminant Analysis</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="lda_qda.html#lda-qda"><span class="std std-ref">Linear and Quadratic Discriminant Analysis</span></a> section for further details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a>([...])</p></td>
<td><p>Linear Discriminant Analysis.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">discriminant_analysis.QuadraticDiscriminantAnalysis</span></code></a>(*)</p></td>
<td><p>Quadratic Discriminant Analysis.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.dummy">
<span id="sklearn-dummy-dummy-estimators"></span><span id="dummy-ref"></span><h2><a class="reference internal" href="#module-sklearn.dummy" title="sklearn.dummy"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.dummy</span></code></a>: Dummy estimators<a class="headerlink" href="#module-sklearn.dummy" title="Permalink to this heading">¶</a></h2>
<p><strong>User guide:</strong> See the <a class="reference internal" href="model_evaluation.html#model-evaluation"><span class="std std-ref">Metrics and scoring: quantifying the quality of predictions</span></a> section for further details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier" title="sklearn.dummy.DummyClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy.DummyClassifier</span></code></a>(*[, strategy, ...])</p></td>
<td><p>DummyClassifier makes predictions that ignore the input features.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.dummy.DummyRegressor.html#sklearn.dummy.DummyRegressor" title="sklearn.dummy.DummyRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dummy.DummyRegressor</span></code></a>(*[, strategy, ...])</p></td>
<td><p>Regressor that makes predictions using simple rules.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
</tbody>
</table>
</section>
<section id="module-sklearn.ensemble">
<span id="sklearn-ensemble-ensemble-methods"></span><span id="ensemble-ref"></span><h2><a class="reference internal" href="#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a>: Ensemble Methods<a class="headerlink" href="#module-sklearn.ensemble" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a> module includes ensemble-based methods for
classification, regression and anomaly detection.</p>
<p><strong>User guide:</strong> See the <a class="reference internal" href="ensemble.html#ensemble"><span class="std std-ref">Ensembles: Gradient boosting, random forests, bagging, voting, stacking</span></a> section for further details.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.AdaBoostClassifier</span></code></a>([estimator, ...])</p></td>
<td><p>An AdaBoost classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.AdaBoostRegressor</span></code></a>([estimator, ...])</p></td>
<td><p>An AdaBoost regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble.BaggingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.BaggingClassifier</span></code></a>([estimator, ...])</p></td>
<td><p>A Bagging classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble.BaggingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.BaggingRegressor</span></code></a>([estimator, ...])</p></td>
<td><p>A Bagging regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble.ExtraTreesClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.ExtraTreesClassifier</span></code></a>([...])</p></td>
<td><p>An extra-trees classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.ExtraTreesRegressor</span></code></a>([n_estimators, ...])</p></td>
<td><p>An extra-trees regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.GradientBoostingClassifier</span></code></a>(*[, ...])</p></td>
<td><p>Gradient Boosting for classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.GradientBoostingRegressor</span></code></a>(*[, ...])</p></td>
<td><p>Gradient Boosting for regression.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest" title="sklearn.ensemble.IsolationForest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.IsolationForest</span></code></a>(*[, n_estimators, ...])</p></td>
<td><p>Isolation Forest Algorithm.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.RandomForestClassifier</span></code></a>([...])</p></td>
<td><p>A random forest classifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.RandomForestRegressor</span></code></a>([...])</p></td>
<td><p>A random forest regressor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.RandomTreesEmbedding</span></code></a>([...])</p></td>
<td><p>An ensemble of totally random trees.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.StackingClassifier.html#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble.StackingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.StackingClassifier</span></code></a>(estimators[, ...])</p></td>
<td><p>Stack of estimators with a final classifier.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.StackingRegressor.html#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble.StackingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.StackingRegressor</span></code></a>(estimators[, ...])</p></td>
<td><p>Stack of estimators with a final regressor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.VotingClassifier</span></code></a>(estimators, *[, ...])</p></td>
<td><p>Soft Voting/Majority Rule classifier for unfitted estimators.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.VotingRegressor.html#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble.VotingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.VotingRegressor</span></code></a>(estimators, *[, ...])</p></td>
<td><p>Prediction voting regressor for unfitted estimators.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.HistGradientBoostingRegressor</span></code></a>([...])</p></td>
<td><p>Histogram-based Gradient Boosting Regression Tree.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble.HistGradientBoostingClassifier</span></code></a>([...])</p></td>
<td><p>Histogram-based Gradient Boosting Classification Tree.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
</tbody>
</table>
</section>
<section id="module-sklearn.exceptions">
<span id="sklearn-exceptions-exceptions-and-warnings"></span><span id="exceptions-ref"></span><h2><a class="reference internal" href="#module-sklearn.exceptions" title="sklearn.exceptions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.exceptions</span></code></a>: Exceptions and warnings<a class="headerlink" href="#module-sklearn.exceptions" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.exceptions" title="sklearn.exceptions"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.exceptions</span></code></a> module includes all custom warnings and error
classes used across scikit-learn.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.ConvergenceWarning.html#sklearn.exceptions.ConvergenceWarning" title="sklearn.exceptions.ConvergenceWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.ConvergenceWarning</span></code></a></p></td>
<td><p>Custom warning to capture convergence problems</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.DataConversionWarning.html#sklearn.exceptions.DataConversionWarning" title="sklearn.exceptions.DataConversionWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.DataConversionWarning</span></code></a></p></td>
<td><p>Warning used to notify implicit data conversions happening in the code.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.DataDimensionalityWarning.html#sklearn.exceptions.DataDimensionalityWarning" title="sklearn.exceptions.DataDimensionalityWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.DataDimensionalityWarning</span></code></a></p></td>
<td><p>Custom warning to notify potential issues with data dimensionality.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.EfficiencyWarning.html#sklearn.exceptions.EfficiencyWarning" title="sklearn.exceptions.EfficiencyWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.EfficiencyWarning</span></code></a></p></td>
<td><p>Warning used to notify the user of inefficient computation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.FitFailedWarning.html#sklearn.exceptions.FitFailedWarning" title="sklearn.exceptions.FitFailedWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.FitFailedWarning</span></code></a></p></td>
<td><p>Warning class used if there is an error while fitting the estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.InconsistentVersionWarning.html#sklearn.exceptions.InconsistentVersionWarning" title="sklearn.exceptions.InconsistentVersionWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.InconsistentVersionWarning</span></code></a>(*, ...)</p></td>
<td><p>Warning raised when an estimator is unpickled with a inconsistent version.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.exceptions.NotFittedError.html#sklearn.exceptions.NotFittedError" title="sklearn.exceptions.NotFittedError"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.NotFittedError</span></code></a></p></td>
<td><p>Exception class to raise if estimator is used before fitting.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.exceptions.UndefinedMetricWarning.html#sklearn.exceptions.UndefinedMetricWarning" title="sklearn.exceptions.UndefinedMetricWarning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exceptions.UndefinedMetricWarning</span></code></a></p></td>
<td><p>Warning used when the metric is invalid</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.experimental">
<span id="sklearn-experimental-experimental"></span><h2><a class="reference internal" href="#module-sklearn.experimental" title="sklearn.experimental"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.experimental</span></code></a>: Experimental<a class="headerlink" href="#module-sklearn.experimental" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.experimental" title="sklearn.experimental"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.experimental</span></code></a> module provides importable modules that enable
the use of experimental features or estimators.</p>
<p>The features and estimators that are experimental aren’t subject to
deprecation cycles. Use them at your own risks!</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/sklearn.experimental.enable_iterative_imputer.html#module-sklearn.experimental.enable_iterative_imputer" title="sklearn.experimental.enable_iterative_imputer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">experimental.enable_iterative_imputer</span></code></a></p></td>
<td><p>Enables IterativeImputer</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/sklearn.experimental.enable_halving_search_cv.html#module-sklearn.experimental.enable_halving_search_cv" title="sklearn.experimental.enable_halving_search_cv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">experimental.enable_halving_search_cv</span></code></a></p></td>
<td><p>Enables Successive Halving search-estimators</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-sklearn.feature_extraction">
<span id="sklearn-feature-extraction-feature-extraction"></span><span id="feature-extraction-ref"></span><h2><a class="reference internal" href="#module-sklearn.feature_extraction" title="sklearn.feature_extraction"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction</span></code></a>: Feature Extraction<a class="headerlink" href="#module-sklearn.feature_extraction" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="#module-sklearn.feature_extraction" title="sklearn.feature_extraction"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_extraction</span></code></a> module deals with feature extraction
from raw data. It currently includes methods to extract features from text and
images.</p>