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<section id="outputcodeclassifier">
<h1>OutputCodeClassifier<a class="headerlink" href="#outputcodeclassifier" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.multiclass.</span></span><span class="sig-name descname"><span class="pre">OutputCodeClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">code_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/multiclass.py#L1039"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier" title="Link to this definition">#</a></dt>
<dd><p>(Error-Correcting) Output-Code multiclass strategy.</p>
<p>Output-code based strategies consist in representing each class with a
binary code (an array of 0s and 1s). At fitting time, one binary
classifier per bit in the code book is fitted. At prediction time, the
classifiers are used to project new points in the class space and the class
closest to the points is chosen. The main advantage of these strategies is
that the number of classifiers used can be controlled by the user, either
for compressing the model (0 < <code class="docutils literal notranslate"><span class="pre">code_size</span></code> < 1) or for making the model more
robust to errors (<code class="docutils literal notranslate"><span class="pre">code_size</span></code> > 1). See the documentation for more details.</p>
<p>Read more in the <a class="reference internal" href="../multiclass.html#ecoc"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>estimator</strong><span class="classifier">estimator object</span></dt><dd><p>An estimator object implementing <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and one of
<a class="reference internal" href="../../glossary.html#term-decision_function"><span class="xref std std-term">decision_function</span></a> or <a class="reference internal" href="../../glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a>.</p>
</dd>
<dt><strong>code_size</strong><span class="classifier">float, default=1.5</span></dt><dd><p>Percentage of the number of classes to be used to create the code book.
A number between 0 and 1 will require fewer classifiers than
one-vs-the-rest. A number greater than 1 will require more classifiers
than one-vs-the-rest.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance, default=None</span></dt><dd><p>The generator used to initialize the codebook.
Pass an int for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation: the multiclass problems
are computed in parallel.</p>
<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>estimators_</strong><span class="classifier">list of <code class="docutils literal notranslate"><span class="pre">int(n_classes</span> <span class="pre">*</span> <span class="pre">code_size)</span></code> estimators</span></dt><dd><p>Estimators used for predictions.</p>
</dd>
<dt><strong>classes_</strong><span class="classifier">ndarray of shape (n_classes,)</span></dt><dd><p>Array containing labels.</p>
</dd>
<dt><strong>code_book_</strong><span class="classifier">ndarray of shape (n_classes, <code class="docutils literal notranslate"><span class="pre">len(estimators_)</span></code>)</span></dt><dd><p>Binary array containing the code of each class.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Only defined if the
underlying estimator exposes such an attribute when fit.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Only defined if the
underlying estimator exposes such an attribute when fit.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneVsRestClassifier</span></code></a></dt><dd><p>One-vs-all multiclass strategy.</p>
</dd>
<dt><a class="reference internal" href="sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OneVsOneClassifier</span></code></a></dt><dd><p>One-vs-one multiclass strategy.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r2eddaeec0849-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>“Solving multiclass learning problems via error-correcting output
codes”,
Dietterich T., Bakiri G.,
Journal of Artificial Intelligence Research 2,
1995.</p>
</div>
<div class="citation" id="r2eddaeec0849-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>“The error coding method and PICTs”,
James G., Hastie T.,
Journal of Computational and Graphical statistics 7,
1998.</p>
</div>
<div class="citation" id="r2eddaeec0849-3" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
<p>“The Elements of Statistical Learning”,
Hastie T., Tibshirani R., Friedman J., page 606 (second-edition)
2008.</p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.multiclass</span><span class="w"> </span><span class="kn">import</span> <span class="n">OutputCodeClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">OutputCodeClassifier</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">estimator</span><span class="o">=</span><span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</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="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="go">array([1])</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/multiclass.py#L1155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit underlying estimators.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Multi-class targets.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters passed to the <code class="docutils literal notranslate"><span class="pre">estimator.fit</span></code> method of each
sub-estimator.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4: </span>Only available if <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code>. See
<a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">Metadata Routing User Guide</span></a> for more
details.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns a fitted instance of self.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/multiclass.py#L1261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRouter</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRouter.html#sklearn.utils.metadata_routing.MetadataRouter" title="sklearn.utils.metadata_routing.MetadataRouter"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRouter</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/multiclass.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict multi-class targets using underlying estimators.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Predicted multi-class targets.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/base.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.score" title="Link to this definition">#</a></dt>
<dd><p>Return <a class="reference internal" href="../model_evaluation.html#accuracy-score"><span class="std std-ref">accuracy</span></a> on provided data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.set_params" title="Link to this definition">#</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="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>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.multiclass.OutputCodeClassifier.set_score_request">
<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.multiclass.OutputCodeClassifier" title="sklearn.multiclass.OutputCodeClassifier"><span class="pre">OutputCodeClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.multiclass.OutputCodeClassifier.set_score_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<a class="reference internal" href="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>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we discuss the problem of classification when the target variable is composed of more than two classes. This is called multiclass classification."><img alt="" src="../../_images/sphx_glr_plot_multiclass_overview_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/multiclass/plot_multiclass_overview.html#sphx-glr-auto-examples-multiclass-plot-multiclass-overview-py"><span class="std std-ref">Overview of multiclass training meta-estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Overview of multiclass training meta-estimators</div>
</div></div></section>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier"><code class="docutils literal notranslate"><span class="pre">OutputCodeClassifier</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.multiclass.OutputCodeClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
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