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              <ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code>.CalibratedClassifierCV</a><ul>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV</span></code></a><ul>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.fit"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.fit</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.get_metadata_routing</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.get_params"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.get_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.predict"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.predict</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.predict_proba</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.score"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.score</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.set_fit_request"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.set_fit_request</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.set_params"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.set_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.set_score_request"><code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV.set_score_request</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#examples-using-sklearn-calibration-calibratedclassifiercv">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.calibration.CalibratedClassifierCV</span></code></a></li>
</ul>
</li>
</ul>

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  <section id="sklearn-calibration-calibratedclassifiercv">
<h1><a class="reference internal" href="../classes.html#module-sklearn.calibration" title="sklearn.calibration"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code></a>.CalibratedClassifierCV<a class="headerlink" href="#sklearn-calibration-calibratedclassifiercv" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">CalibratedClassifierCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sigmoid'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</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>, <em class="sig-param"><span class="n"><span class="pre">ensemble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/calibration.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV" title="Permalink to this definition">¶</a></dt>
<dd><p>Probability calibration with isotonic regression or logistic regression.</p>
<p>This class uses cross-validation to both estimate the parameters of a
classifier and subsequently calibrate a classifier. With default
<code class="docutils literal notranslate"><span class="pre">ensemble=True</span></code>, for each cv split it
fits a copy of the base estimator to the training subset, and calibrates it
using the testing subset. For prediction, predicted probabilities are
averaged across these individual calibrated classifiers. When
<code class="docutils literal notranslate"><span class="pre">ensemble=False</span></code>, cross-validation is used to obtain unbiased predictions,
via <a class="reference internal" href="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">cross_val_predict</span></code></a>, which are then
used for calibration. For prediction, the base estimator, trained using all
the data, is used. This is the method implemented when <code class="docutils literal notranslate"><span class="pre">probabilities=True</span></code>
for <a class="reference internal" href="../classes.html#module-sklearn.svm" title="sklearn.svm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.svm</span></code></a> estimators.</p>
<p>Already fitted classifiers can be calibrated via the parameter
<code class="docutils literal notranslate"><span class="pre">cv=&quot;prefit&quot;</span></code>. In this case, no cross-validation is used and all provided
data is used for calibration. The user has to take care manually that data
for model fitting and calibration are disjoint.</p>
<p>The calibration is based on the <a class="reference internal" href="../../glossary.html#term-decision_function"><span class="xref std std-term">decision_function</span></a> method of the
<code class="docutils literal notranslate"><span class="pre">estimator</span></code> if it exists, else on <a class="reference internal" href="../../glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a>.</p>
<p>Read more in the <a class="reference internal" href="../calibration.html#calibration"><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 instance, default=None</span></dt><dd><p>The classifier whose output need to be calibrated to provide more
accurate <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> outputs. The default classifier is
a <a class="reference internal" href="sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearSVC</span></code></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.2.</span></p>
</div>
</dd>
<dt><strong>method</strong><span class="classifier">{‘sigmoid’, ‘isotonic’}, default=’sigmoid’</span></dt><dd><p>The method to use for calibration. Can be ‘sigmoid’ which
corresponds to Platt’s method (i.e. a logistic regression model) or
‘isotonic’ which is a non-parametric approach. It is not advised to
use isotonic calibration with too few calibration samples
<code class="docutils literal notranslate"><span class="pre">(&lt;&lt;1000)</span></code> since it tends to overfit.</p>
</dd>
<dt><strong>cv</strong><span class="classifier">int, cross-validation generator, iterable or “prefit”,             default=None</span></dt><dd><p>Determines the cross-validation splitting strategy.
Possible inputs for cv are:</p>
<ul class="simple">
<li><p>None, to use the default 5-fold cross-validation,</p></li>
<li><p>integer, to specify the number of folds.</p></li>
<li><p><a class="reference internal" href="../../glossary.html#term-CV-splitter"><span class="xref std std-term">CV splitter</span></a>,</p></li>
<li><p>An iterable yielding (train, test) splits as arrays of indices.</p></li>
</ul>
<p>For integer/None inputs, if <code class="docutils literal notranslate"><span class="pre">y</span></code> is binary or multiclass,
<a class="reference internal" href="sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">StratifiedKFold</span></code></a> is used. If <code class="docutils literal notranslate"><span class="pre">y</span></code> is
neither binary nor multiclass, <a class="reference internal" href="sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code></a>
is used.</p>
<p>Refer to the <a class="reference internal" href="../cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a> for the various
cross-validation strategies that can be used here.</p>
<p>If “prefit” is passed, it is assumed that <code class="docutils literal notranslate"><span class="pre">estimator</span></code> has been
fitted already and all data is used for calibration.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.22: </span><code class="docutils literal notranslate"><span class="pre">cv</span></code> default value if None changed from 3-fold to 5-fold.</p>
</div>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of jobs to run in parallel.
<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 v1.3.1)"><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.</p>
<p>Base estimator clones are fitted in parallel across cross-validation
iterations. Therefore parallelism happens only when <code class="docutils literal notranslate"><span class="pre">cv</span> <span class="pre">!=</span> <span class="pre">&quot;prefit&quot;</span></code>.</p>
<p>See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
<dt><strong>ensemble</strong><span class="classifier">bool, default=True</span></dt><dd><p>Determines how the calibrator is fitted when <code class="docutils literal notranslate"><span class="pre">cv</span></code> is not <code class="docutils literal notranslate"><span class="pre">'prefit'</span></code>.
Ignored if <code class="docutils literal notranslate"><span class="pre">cv='prefit'</span></code>.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, the <code class="docutils literal notranslate"><span class="pre">estimator</span></code> is fitted using training data, and
calibrated using testing data, for each <code class="docutils literal notranslate"><span class="pre">cv</span></code> fold. The final estimator
is an ensemble of <code class="docutils literal notranslate"><span class="pre">n_cv</span></code> fitted classifier and calibrator pairs, where
<code class="docutils literal notranslate"><span class="pre">n_cv</span></code> is the number of cross-validation folds. The output is the
average predicted probabilities of all pairs.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">False</span></code>, <code class="docutils literal notranslate"><span class="pre">cv</span></code> is used to compute unbiased predictions, via
<a class="reference internal" href="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">cross_val_predict</span></code></a>, which are then
used for calibration. At prediction time, the classifier used is the
<code class="docutils literal notranslate"><span class="pre">estimator</span></code> trained on all the data.
Note that this method is also internally implemented  in
<a class="reference internal" href="../classes.html#module-sklearn.svm" title="sklearn.svm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.svm</span></code></a> estimators with the <code class="docutils literal notranslate"><span class="pre">probabilities=True</span></code> parameter.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.24.</span></p>
</div>
</dd>
<dt><strong>base_estimator</strong><span class="classifier">estimator instance</span></dt><dd><p>This parameter is deprecated. Use <code class="docutils literal notranslate"><span class="pre">estimator</span></code> instead.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 1.2: </span>The parameter <code class="docutils literal notranslate"><span class="pre">base_estimator</span></code> is deprecated in 1.2 and will be
removed in 1.4. Use <code class="docutils literal notranslate"><span class="pre">estimator</span></code> instead.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>classes_</strong><span class="classifier">ndarray of shape (n_classes,)</span></dt><dd><p>The class labels.</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">New 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">New in version 1.0.</span></p>
</div>
</dd>
<dt><strong>calibrated_classifiers_</strong><span class="classifier">list (len() equal to cv or 1 if <code class="docutils literal notranslate"><span class="pre">cv=&quot;prefit&quot;</span></code>             or <code class="docutils literal notranslate"><span class="pre">ensemble=False</span></code>)</span></dt><dd><p>The list of classifier and calibrator pairs.</p>
<ul class="simple">
<li><p>When <code class="docutils literal notranslate"><span class="pre">cv=&quot;prefit&quot;</span></code>, the fitted <code class="docutils literal notranslate"><span class="pre">estimator</span></code> and fitted
calibrator.</p></li>
<li><p>When <code class="docutils literal notranslate"><span class="pre">cv</span></code> is not “prefit” and <code class="docutils literal notranslate"><span class="pre">ensemble=True</span></code>, <code class="docutils literal notranslate"><span class="pre">n_cv</span></code> fitted
<code class="docutils literal notranslate"><span class="pre">estimator</span></code> and calibrator pairs. <code class="docutils literal notranslate"><span class="pre">n_cv</span></code> is the number of
cross-validation folds.</p></li>
<li><p>When <code class="docutils literal notranslate"><span class="pre">cv</span></code> is not “prefit” and <code class="docutils literal notranslate"><span class="pre">ensemble=False</span></code>, the <code class="docutils literal notranslate"><span class="pre">estimator</span></code>,
fitted on all the data, and fitted calibrator.</p></li>
</ul>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.24: </span>Single calibrated classifier case when <code class="docutils literal notranslate"><span class="pre">ensemble=False</span></code>.</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.calibration.calibration_curve.html#sklearn.calibration.calibration_curve" title="sklearn.calibration.calibration_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calibration_curve</span></code></a></dt><dd><p>Compute true and predicted probabilities for a calibration curve.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r57cf438d7060-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Obtaining calibrated probability estimates from decision trees
and naive Bayesian classifiers, B. Zadrozny &amp; C. Elkan, ICML 2001</p>
</div>
<div class="citation" id="r57cf438d7060-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>Transforming Classifier Scores into Accurate Multiclass
Probability Estimates, B. Zadrozny &amp; C. Elkan, (KDD 2002)</p>
</div>
<div class="citation" id="r57cf438d7060-3" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
<p>Probabilistic Outputs for Support Vector Machines and Comparisons to
Regularized Likelihood Methods, J. Platt, (1999)</p>
</div>
<div class="citation" id="r57cf438d7060-4" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></span>
<p>Predicting Good Probabilities with Supervised Learning,
A. Niculescu-Mizil &amp; R. Caruana, ICML 2005</p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibratedClassifierCV</span>
<span class="gp">&gt;&gt;&gt; </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">2</span><span class="p">,</span>
<span class="gp">... </span>                           <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">base_clf</span> <span class="o">=</span> <span class="n">GaussianNB</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</span> <span class="o">=</span> <span class="n">CalibratedClassifierCV</span><span class="p">(</span><span class="n">base_clf</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</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="go">CalibratedClassifierCV(...)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">calibrated_classifiers_</span><span class="p">)</span>
<span class="go">3</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:]</span>
<span class="go">array([[0.110..., 0.889...],</span>
<span class="go">       [0.072..., 0.927...],</span>
<span class="go">       [0.928..., 0.071...],</span>
<span class="go">       [0.928..., 0.071...],</span>
<span class="go">       [0.071..., 0.928...]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </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">2</span><span class="p">,</span>
<span class="gp">... </span>                           <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_calib</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_calib</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="gp">... </span>       <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">base_clf</span> <span class="o">=</span> <span class="n">GaussianNB</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">base_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">GaussianNB()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</span> <span class="o">=</span> <span class="n">CalibratedClassifierCV</span><span class="p">(</span><span class="n">base_clf</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="s2">&quot;prefit&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_calib</span><span class="p">,</span> <span class="n">y_calib</span><span class="p">)</span>
<span class="go">CalibratedClassifierCV(...)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">calibrated_classifiers_</span><span class="p">)</span>
<span class="go">1</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">calibrated_clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">([[</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]])</span>
<span class="go">array([[0.936..., 0.063...]])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="sklearn.calibration.CalibratedClassifierCV.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y[, sample_weight])</p></td>
<td><p>Fit the calibrated model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.get_metadata_routing" title="sklearn.calibration.CalibratedClassifierCV.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="sklearn.calibration.CalibratedClassifierCV.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="sklearn.calibration.CalibratedClassifierCV.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X)</p></td>
<td><p>Predict the target of new samples.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="sklearn.calibration.CalibratedClassifierCV.predict_proba"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_proba</span></code></a>(X)</p></td>
<td><p>Calibrated probabilities of classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.score" title="sklearn.calibration.CalibratedClassifierCV.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
<td><p>Return the mean accuracy on the given test data and labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.set_fit_request" title="sklearn.calibration.CalibratedClassifierCV.set_fit_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_fit_request</span></code></a>(*[, sample_weight])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="sklearn.calibration.CalibratedClassifierCV.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV.set_score_request" title="sklearn.calibration.CalibratedClassifierCV.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, sample_weight])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.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="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>, <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/702316c27/sklearn/calibration.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the calibrated model.</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>Training data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters to pass to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method of the underlying
classifier.</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>Returns an instance of self.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.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/702316c27/sklearn/calibration.py#L526"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_metadata_routing" title="Permalink 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>
<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.calibration.CalibratedClassifierCV.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/702316c27/sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="Permalink 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.calibration.CalibratedClassifierCV.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/702316c27/sklearn/calibration.py#L507"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict the target of new samples.</p>
<p>The predicted class is the class that has the highest probability,
and can thus be different from the prediction of the uncalibrated classifier.</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>The samples, as accepted by <code class="docutils literal notranslate"><span class="pre">estimator.predict</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>C</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The predicted class.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</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/702316c27/sklearn/calibration.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Calibrated probabilities of classification.</p>
<p>This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.</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>The samples, as accepted by <code class="docutils literal notranslate"><span class="pre">estimator.predict_proba</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>C</strong><span class="classifier">ndarray of shape (n_samples, n_classes)</span></dt><dd><p>The predicted probas.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.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/702316c27/sklearn/base.py#L680"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the mean accuracy on the given test 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.calibration.CalibratedClassifierCV.set_fit_request">
<span class="sig-name descname"><span class="pre">set_fit_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/typing.html#typing.Union" title="(in Python v3.11)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><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 v3.11)"><span class="pre">None</span></a><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 v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></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">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><span class="pre">CalibratedClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1077"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_fit_request" title="Permalink to this definition">¶</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</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">fit</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">fit</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">New 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">fit</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>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibratedClassifierCV.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/702316c27/sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="Permalink 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">&lt;component&gt;__&lt;parameter&gt;</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.calibration.CalibratedClassifierCV.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/typing.html#typing.Union" title="(in Python v3.11)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><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 v3.11)"><span class="pre">None</span></a><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 v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></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">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><span class="pre">CalibratedClassifierCV</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1077"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_score_request" title="Permalink 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">New 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="examples-using-sklearn-calibration-calibratedclassifiercv">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.calibration.CalibratedClassifierCV</span></code><a class="headerlink" href="#examples-using-sklearn-calibration-calibratedclassifiercv" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="When performing classification one often wants to predict not only the class label, but also th..."><img alt="" src="../../_images/sphx_glr_plot_calibration_curve_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py"><span class="std std-ref">Probability Calibration curves</span></a></p>
  <div class="sphx-glr-thumbnail-title">Probability Calibration curves</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class ..."><img alt="" src="../../_images/sphx_glr_plot_calibration_multiclass_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/calibration/plot_calibration_multiclass.html#sphx-glr-auto-examples-calibration-plot-calibration-multiclass-py"><span class="std std-ref">Probability Calibration for 3-class classification</span></a></p>
  <div class="sphx-glr-thumbnail-title">Probability Calibration for 3-class classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification you often want to predict not only the class label, but also the..."><img alt="" src="../../_images/sphx_glr_plot_calibration_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py"><span class="std std-ref">Probability calibration of classifiers</span></a></p>
  <div class="sphx-glr-thumbnail-title">Probability calibration of classifiers</div>
</div></div><div class="clearer"></div></section>
</section>


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