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<li><a class="reference internal" href="#">1. Metadata Routing</a><ul>
<li><a class="reference internal" href="#usage-examples">1.1. Usage Examples</a><ul>
<li><a class="reference internal" href="#weighted-scoring-and-fitting">1.1.1. Weighted scoring and fitting</a></li>
<li><a class="reference internal" href="#weighted-scoring-and-unweighted-fitting">1.1.2. Weighted scoring and unweighted fitting</a></li>
<li><a class="reference internal" href="#unweighted-feature-selection">1.1.3. Unweighted feature selection</a></li>
<li><a class="reference internal" href="#advanced-different-scoring-and-fitting-weights">1.1.4. Advanced: Different scoring and fitting weights</a></li>
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<li><a class="reference internal" href="#api-interface">1.2. API Interface</a></li>
<li><a class="reference internal" href="#metadata-routing-support-status">1.3. Metadata Routing Support Status</a></li>
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<span class="target" id="metadata-routing"></span><section id="id1">
<h1><span class="section-number">1. </span>Metadata Routing<a class="headerlink" href="#id1" title="Permalink 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 implemented yet for many
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 this feature 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>
</div>
<p>This guide demonstrates how metadata such as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> can be routed
and passed along to estimators, scorers, and CV splitters through
meta-estimators 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> and
<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>. In order to pass metadata to a method
such as <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. For estimators and splitters, this is done via <code class="docutils literal notranslate"><span class="pre">set_*_request</span></code> methods,
e.g. <code class="docutils literal notranslate"><span class="pre">set_fit_request(...)</span></code>, and for scorers this is done via the
<code class="docutils literal notranslate"><span class="pre">set_score_request</span></code> method. 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>
<p>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>
<div class="admonition note">
<p class="admonition-title">Note</p>
<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>
<section id="usage-examples">
<h2><span class="section-number">1.1. </span>Usage Examples<a class="headerlink" href="#usage-examples" title="Permalink to this heading">¶</a></h2>
<p>Here we present a few examples to show different common use-cases. 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="Permalink to this heading">¶</a></h3>
<p>Here <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 weights for our scorer and the internal cross validation of
<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 of these <em>consumers</em> know how to use
metadata called <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>:</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="kc">True</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="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, <code class="docutils literal notranslate"><span class="pre">my_weights</span></code> is passed 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>Error handling: if <code class="docutils literal notranslate"><span class="pre">params={"sample_weigh":</span> <span class="pre">my_weights,</span> <span class="pre">...}</span></code> were passed
(note the typo), <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,
since <code class="docutils literal notranslate"><span class="pre">sample_weigh</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="Permalink to this heading">¶</a></h3>
<p>When passing metadata such as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> around, 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 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>) when used in another
<a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">router</span></a> such as a <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 <code class="docutils literal notranslate"><span class="pre">*GridSearchCV</span></code>. 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="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="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> has 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> will raise an
error because <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is passed in 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="Permalink to this heading">¶</a></h3>
<p>Setting request values for metadata are only required if the object, e.g. estimator,
scorer, etc., is a consumer of that metadata 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>
doesn’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="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="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="advanced-different-scoring-and-fitting-weights">
<h3><span class="section-number">1.1.4. </span>Advanced: Different scoring and fitting weights<a class="headerlink" href="#advanced-different-scoring-and-fitting-weights" title="Permalink 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="Permalink 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
(<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
(the child estimator, 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_*_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>: if this estimator is used in a meta-estimator, the
meta-estimator should forward <code class="docutils literal notranslate"><span class="pre">"param_name"</span></code> as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> to this
estimator. 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 what is 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 object, e.g.
estimator, 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 for</span>
<span class="go">LogisticRegression.score</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 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="Permalink 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.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.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.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.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.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>
</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.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.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>
<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.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.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.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.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.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.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.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.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>
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