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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="model_selection.html">3. Model selection and evaluation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l1"><a class="reference internal" href="visualizations.html">5. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="metadata-routing">
<span id="id1"></span><h1><span class="section-number">1. </span>Metadata Routing<a class="headerlink" href="#metadata-routing" title="Link to this heading">#</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The Metadata Routing API is experimental, and is not yet implemented for all
estimators. Please refer to the <a class="reference internal" href="#metadata-routing-models"><span class="std std-ref">list of supported and unsupported
models</span></a> for more information. It may change without
the usual deprecation cycle. By default this feature is not enabled. You can
enable it by setting the <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing</span></code> flag to
<code class="docutils literal notranslate"><span class="pre">True</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">sklearn</span>
<span class="gp">>>> </span><span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="n">enable_metadata_routing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that the methods and requirements introduced in this document are only
relevant if you want to pass <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> (e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>) to a method.
If you’re only passing <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> and no other parameter / metadata to
methods such as <a class="reference internal" href="glossary.html#term-fit"><span class="xref std std-term">fit</span></a>, <a class="reference internal" href="glossary.html#term-transform"><span class="xref std std-term">transform</span></a>, etc., then you don’t need to set
anything.</p>
</div>
<p>This guide demonstrates how <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> can be routed and passed between objects in
scikit-learn. If you are developing a scikit-learn compatible estimator or
meta-estimator, you can check our related developer guide:
<a class="reference internal" href="auto_examples/miscellaneous/plot_metadata_routing.html#sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Metadata Routing</span></a>.</p>
<p>Metadata is data that an estimator, scorer, or CV splitter takes into account if the
user explicitly passes it as a parameter. For instance, <a class="reference internal" href="modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-class docutils literal notranslate"><span class="pre">KMeans</span></code></a> accepts
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in its <code class="docutils literal notranslate"><span class="pre">fit()</span></code> method and considers it to calculate its centroids.
<code class="docutils literal notranslate"><span class="pre">classes</span></code> are consumed by some classifiers and <code class="docutils literal notranslate"><span class="pre">groups</span></code> are used in some splitters, but
any data that is passed into an object’s methods apart from X and y can be considered as
metadata. Prior to scikit-learn version 1.3, there was no single API for passing
metadata like that if these objects were used in conjunction with other objects, e.g. a
scorer accepting <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> inside a <a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>.</p>
<p>With the Metadata Routing API, we can transfer metadata to estimators, scorers, and CV
splitters using <a class="reference internal" href="glossary.html#term-meta-estimators"><span class="xref std std-term">meta-estimators</span></a> (such as <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> or
<a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>) or functions such as
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> which route data to other objects. In order to
pass metadata to a method like <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">score</span></code>, the object consuming the metadata,
must <em>request</em> it. This is done via <code class="docutils literal notranslate"><span class="pre">set_{method}_request()</span></code> methods, where <code class="docutils literal notranslate"><span class="pre">{method}</span></code>
is substituted by the name of the method that requests the metadata. For instance,
estimators that use the metadata in their <code class="docutils literal notranslate"><span class="pre">fit()</span></code> method would use <code class="docutils literal notranslate"><span class="pre">set_fit_request()</span></code>,
and scorers would use <code class="docutils literal notranslate"><span class="pre">set_score_request()</span></code>. These methods allow us to specify which
metadata to request, for instance <code class="docutils literal notranslate"><span class="pre">set_fit_request(sample_weight=True)</span></code>.</p>
<p>For grouped splitters such as <a class="reference internal" href="modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>, a
<code class="docutils literal notranslate"><span class="pre">groups</span></code> parameter is requested by default. This is best demonstrated by the
following examples.</p>
<section id="usage-examples">
<h2><span class="section-number">1.1. </span>Usage Examples<a class="headerlink" href="#usage-examples" title="Link to this heading">#</a></h2>
<p>Here we present a few examples to show some common use-cases. Our goal is to pass
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> and <code class="docutils literal notranslate"><span class="pre">groups</span></code> through <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a>, which
routes the metadata to <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> and to a custom scorer
made with <a class="reference internal" href="modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a>, both of which <em>can</em> use the metadata in their
methods. In these examples we want to individually set whether to use the metadata
within the different <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a>.</p>
<p>The examples in this section require the following imports and data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegressionCV</span><span class="p">,</span> <span class="n">LogisticRegression</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_validate</span><span class="p">,</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">GroupKFold</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">SelectKBest</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">>>> </span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">4</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_groups</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_weights</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_other_weights</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
</pre></div>
</div>
<section id="weighted-scoring-and-fitting">
<h3><span class="section-number">1.1.1. </span>Weighted scoring and fitting<a class="headerlink" href="#weighted-scoring-and-fitting" title="Link to this heading">#</a></h3>
<p>The splitter used internally in <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>, requests <code class="docutils literal notranslate"><span class="pre">groups</span></code> by default. However, we need
to explicitly request <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> for it and for our custom scorer by specifying
<code class="docutils literal notranslate"><span class="pre">sample_weight=True</span></code> in <code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV`s</span> <span class="pre">`set_fit_request()</span></code>
method and in <code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer`s</span> <span class="pre">`set_score_request</span></code> method. Both
<a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a> know how to use <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in their <code class="docutils literal notranslate"><span class="pre">fit()</span></code> or
<code class="docutils literal notranslate"><span class="pre">score()</span></code> methods. We can then pass the metadata in
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> which will route it to any active consumers:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>Note that in this example, <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> routes <code class="docutils literal notranslate"><span class="pre">my_weights</span></code>
to both the scorer and <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>.</p>
<p>If we would pass <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in the params of
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a>, but not set any object to request it,
<code class="docutils literal notranslate"><span class="pre">UnsetMetadataPassedError</span></code> would be raised, hinting to us that we need to explicitly set
where to route it. The same applies if <code class="docutils literal notranslate"><span class="pre">params={"sample_weights":</span> <span class="pre">my_weights,</span> <span class="pre">...}</span></code>
were passed (note the typo, i.e. <code class="docutils literal notranslate"><span class="pre">weights</span></code> instead of <code class="docutils literal notranslate"><span class="pre">weight</span></code>), since
<code class="docutils literal notranslate"><span class="pre">sample_weights</span></code> was not requested by any of its underlying objects.</p>
</section>
<section id="weighted-scoring-and-unweighted-fitting">
<h3><span class="section-number">1.1.2. </span>Weighted scoring and unweighted fitting<a class="headerlink" href="#weighted-scoring-and-unweighted-fitting" title="Link to this heading">#</a></h3>
<p>When passing metadata such as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> into a <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">router</span></a>
(<a class="reference internal" href="glossary.html#term-meta-estimators"><span class="xref std std-term">meta-estimators</span></a> or routing function), all <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a> require weights to be either explicitly requested or explicitly not
requested (i.e. <code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code>). Thus, to perform an unweighted fit, we need to
configure <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> to not request sample weights, so
that <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> does not pass the weights along:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>If <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV.set_fit_request" title="sklearn.linear_model.LogisticRegressionCV.set_fit_request"><code class="xref py py-meth docutils literal notranslate"><span class="pre">linear_model.LogisticRegressionCV.set_fit_request</span></code></a> had not been called,
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> would raise an error because <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>
is passed but <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> would not be explicitly
configured to recognize the weights.</p>
</section>
<section id="unweighted-feature-selection">
<h3><span class="section-number">1.1.3. </span>Unweighted feature selection<a class="headerlink" href="#unweighted-feature-selection" title="Link to this heading">#</a></h3>
<p>Routing metadata is only possible if the object’s method knows how to use the metadata,
which in most cases means they have it as an explicit parameter. Only then we can set
request values for metadata using <code class="docutils literal notranslate"><span class="pre">set_fit_request(sample_weight=True)</span></code>, for instance.
This makes the object a <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a>.</p>
<p>Unlike <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest"><code class="xref py py-class docutils literal notranslate"><span class="pre">SelectKBest</span></code></a> can’t consume weights and therefore no request
value for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> on its instance is set and <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is not routed
to it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">sel</span> <span class="o">=</span> <span class="n">SelectKBest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">sel</span><span class="p">,</span> <span class="n">lr</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">pipe</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="different-scoring-and-fitting-weights">
<h3><span class="section-number">1.1.4. </span>Different scoring and fitting weights<a class="headerlink" href="#different-scoring-and-fitting-weights" title="Link to this heading">#</a></h3>
<p>Despite <a class="reference internal" href="modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> both expecting the key
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>, we can use aliases to pass different weights to different
consumers. In this example, we pass <code class="docutils literal notranslate"><span class="pre">scoring_weight</span></code> to the scorer, and
<code class="docutils literal notranslate"><span class="pre">fitting_weight</span></code> to <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="s2">"scoring_weight"</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="s2">"fitting_weight"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span>
<span class="gp">... </span> <span class="s2">"scoring_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span>
<span class="gp">... </span> <span class="s2">"fitting_weight"</span><span class="p">:</span> <span class="n">my_other_weights</span><span class="p">,</span>
<span class="gp">... </span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">,</span>
<span class="gp">... </span> <span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="api-interface">
<h2><span class="section-number">1.2. </span>API Interface<a class="headerlink" href="#api-interface" title="Link to this heading">#</a></h2>
<p>A <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a> is an object (estimator, meta-estimator, scorer, splitter) which
accepts and uses some <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> in at least one of its methods (for instance
<code class="docutils literal notranslate"><span class="pre">fit</span></code>, <code class="docutils literal notranslate"><span class="pre">predict</span></code>, <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>, <code class="docutils literal notranslate"><span class="pre">transform</span></code>, <code class="docutils literal notranslate"><span class="pre">score</span></code>, <code class="docutils literal notranslate"><span class="pre">split</span></code>).
Meta-estimators which only forward the metadata to other objects (child estimators,
scorers, or splitters) and don’t use the metadata themselves are not consumers.
(Meta-)Estimators which route metadata to other objects are <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">routers</span></a>.
A(n) (meta-)estimator can be a <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a> and a <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">router</span></a> at the same time.
(Meta-)Estimators and splitters expose a <code class="docutils literal notranslate"><span class="pre">set_{method}_request</span></code> method for each method
which accepts at least one metadata. For instance, if an estimator supports
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">score</span></code>, it exposes
<code class="docutils literal notranslate"><span class="pre">estimator.set_fit_request(sample_weight=value)</span></code> and
<code class="docutils literal notranslate"><span class="pre">estimator.set_score_request(sample_weight=value)</span></code>. Here <code class="docutils literal notranslate"><span class="pre">value</span></code> can be:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: method requests a <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. This means if the metadata is provided,
it will be used, otherwise no error is raised.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: method does not request a <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: router will raise an error if <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is passed. This is in almost
all cases the default value when an object is instantiated and ensures the user sets
the metadata requests explicitly when a metadata is passed. The only exception are
<code class="docutils literal notranslate"><span class="pre">Group*Fold</span></code> splitters.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"param_name"</span></code>: alias for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> if we want to pass different weights to
different consumers. If aliasing is used the meta-estimator should not forward
<code class="docutils literal notranslate"><span class="pre">"param_name"</span></code> to the consumer, but <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> instead, because the consumer
will expect a param called <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. This means the mapping between the
metadata required by the object, e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> and the variable name provided
by the user, e.g. <code class="docutils literal notranslate"><span class="pre">my_weights</span></code> is done at the router level, and not by the consuming
object itself.</p></li>
</ul>
<p>Metadata are requested in the same way for scorers using <code class="docutils literal notranslate"><span class="pre">set_score_request</span></code>.</p>
<p>If a metadata, e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>, is passed by the user, the metadata request for
all objects which potentially can consume <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> should be set by the user,
otherwise an error is raised by the router object. For example, the following code
raises an error, since it hasn’t been explicitly specified whether <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>
should be passed to the estimator’s scorer or not:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"C"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">try</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">GridSearchCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">estimator</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span>
<span class="gp">... </span> <span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">my_weights</span><span class="p">)</span>
<span class="gp">... </span><span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="go">[sample_weight] are passed but are not explicitly set as requested or not</span>
<span class="go">requested for LogisticRegression.score, which is used within GridSearchCV.fit.</span>
<span class="go">Call `LogisticRegression.set_score_request({metadata}=True/False)` for each metadata</span>
<span class="go">you want to request/ignore.</span>
</pre></div>
</div>
<p>The issue can be fixed by explicitly setting the request value:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<p>At the end of the <strong>Usage Examples</strong> section, we disable the configuration flag for
metadata routing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="n">enable_metadata_routing</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="metadata-routing-support-status">
<span id="metadata-routing-models"></span><h2><span class="section-number">1.3. </span>Metadata Routing Support Status<a class="headerlink" href="#metadata-routing-support-status" title="Link to this heading">#</a></h2>
<p>All consumers (i.e. simple estimators which only consume metadata and don’t
route them) support metadata routing, meaning they can be used inside
meta-estimators which support metadata routing. However, development of support
for metadata routing for meta-estimators is in progress, and here is a list of
meta-estimators and tools which support and don’t yet support metadata routing.</p>
<p>Meta-estimators and functions supporting metadata routing:</p>
<ul class="simple">
<li><p><a class="reference internal" href="modules/generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.calibration.CalibratedClassifierCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.compose.ColumnTransformer</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.compose.TransformedTargetRegressor.html#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose.TransformedTargetRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.compose.TransformedTargetRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.covariance.GraphicalLassoCV.html#sklearn.covariance.GraphicalLassoCV" title="sklearn.covariance.GraphicalLassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.covariance.GraphicalLassoCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.StackingClassifier.html#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble.StackingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.StackingClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.StackingRegressor.html#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble.StackingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.StackingRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.VotingClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.VotingRegressor.html#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble.VotingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.VotingRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble.BaggingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.BaggingClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble.BaggingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.BaggingRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.RFE.html#sklearn.feature_selection.RFE" title="sklearn.feature_selection.RFE"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.RFE</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV" title="sklearn.feature_selection.RFECV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.RFECV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel" title="sklearn.feature_selection.SelectFromModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.SelectFromModel</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html#sklearn.feature_selection.SequentialFeatureSelector" title="sklearn.feature_selection.SequentialFeatureSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.SequentialFeatureSelector</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.impute.IterativeImputer</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.ElasticNetCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LarsCV.html#sklearn.linear_model.LarsCV" title="sklearn.linear_model.LarsCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LarsCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV" title="sklearn.linear_model.LassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LassoCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LassoLarsCV.html#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model.LassoLarsCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LassoLarsCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LogisticRegressionCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model.MultiTaskElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.MultiTaskElasticNetCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.MultiTaskLassoCV.html#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model.MultiTaskLassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.MultiTaskLassoCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="sklearn.linear_model.OrthogonalMatchingPursuitCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.OrthogonalMatchingPursuitCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.RANSACRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.RidgeClassifierCV.html#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model.RidgeClassifierCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.RidgeClassifierCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.RidgeCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.GridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.HalvingGridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.HalvingRandomSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.RandomizedSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.permutation_test_score.html#sklearn.model_selection.permutation_test_score" title="sklearn.model_selection.permutation_test_score"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.permutation_test_score</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_validate</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_val_score</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_predict.html#sklearn.model_selection.cross_val_predict" title="sklearn.model_selection.cross_val_predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_val_predict</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.learning_curve.html#sklearn.model_selection.learning_curve" title="sklearn.model_selection.learning_curve"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.learning_curve</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="sklearn.model_selection.validation_curve"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.validation_curve</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OneVsOneClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OneVsRestClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multiclass.OutputCodeClassifier.html#sklearn.multiclass.OutputCodeClassifier" title="sklearn.multiclass.OutputCodeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OutputCodeClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.ClassifierChain.html#sklearn.multioutput.ClassifierChain" title="sklearn.multioutput.ClassifierChain"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.ClassifierChain</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.MultiOutputClassifier.html#sklearn.multioutput.MultiOutputClassifier" title="sklearn.multioutput.MultiOutputClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.MultiOutputClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.MultiOutputRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.RegressorChain.html#sklearn.multioutput.RegressorChain" title="sklearn.multioutput.RegressorChain"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.RegressorChain</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.pipeline.FeatureUnion</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.pipeline.Pipeline</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html#sklearn.semi_supervised.SelfTrainingClassifier" title="sklearn.semi_supervised.SelfTrainingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.semi_supervised.SelfTrainingClassifier</span></code></a></p></li>
</ul>
<p>Meta-estimators and tools not supporting metadata routing yet:</p>
<ul class="simple">
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.AdaBoostClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.AdaBoostRegressor</span></code></a></p></li>
</ul>
</section>
</section>
</article>