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  <section id="calibrationdisplay">
<h1>CalibrationDisplay<a class="headerlink" href="#calibrationdisplay" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay">
<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">CalibrationDisplay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prob_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</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">estimator_name</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">pos_label</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/calibration.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay" title="Link to this definition">#</a></dt>
<dd><p>Calibration curve (also known as reliability diagram) visualization.</p>
<p>It is recommended to use
<a class="reference internal" href="#sklearn.calibration.CalibrationDisplay.from_estimator" title="sklearn.calibration.CalibrationDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> or
<a class="reference internal" href="#sklearn.calibration.CalibrationDisplay.from_predictions" title="sklearn.calibration.CalibrationDisplay.from_predictions"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_predictions</span></code></a>
to create a <code class="docutils literal notranslate"><span class="pre">CalibrationDisplay</span></code>. All parameters are stored as attributes.</p>
<p>Read more about calibration in the <a class="reference internal" href="../calibration.html#calibration"><span class="std std-ref">User Guide</span></a> and
more about the scikit-learn visualization API in <a class="reference internal" href="../../visualizations.html#visualizations"><span class="std std-ref">Visualizations</span></a>.</p>
<p>For an example on how to use the visualization, see
<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="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>prob_true</strong><span class="classifier">ndarray of shape (n_bins,)</span></dt><dd><p>The proportion of samples whose class is the positive class (fraction
of positives), in each bin.</p>
</dd>
<dt><strong>prob_pred</strong><span class="classifier">ndarray of shape (n_bins,)</span></dt><dd><p>The mean predicted probability in each bin.</p>
</dd>
<dt><strong>y_prob</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Probability estimates for the positive class, for each sample.</p>
</dd>
<dt><strong>estimator_name</strong><span class="classifier">str, default=None</span></dt><dd><p>Name of estimator. If None, the estimator name is not shown.</p>
</dd>
<dt><strong>pos_label</strong><span class="classifier">int, float, bool or str, default=None</span></dt><dd><p>The positive class when computing the calibration curve.
By default, <code class="docutils literal notranslate"><span class="pre">pos_label</span></code> is set to <code class="docutils literal notranslate"><span class="pre">estimators.classes_[1]</span></code> when using
<code class="docutils literal notranslate"><span class="pre">from_estimator</span></code> and set to 1 when using <code class="docutils literal notranslate"><span class="pre">from_predictions</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.1.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>line_</strong><span class="classifier">matplotlib Artist</span></dt><dd><p>Calibration curve.</p>
</dd>
<dt><strong>ax_</strong><span class="classifier">matplotlib Axes</span></dt><dd><p>Axes with calibration curve.</p>
</dd>
<dt><strong>figure_</strong><span class="classifier">matplotlib Figure</span></dt><dd><p>Figure containing the curve.</p>
</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>
<dt><a class="reference internal" href="#sklearn.calibration.CalibrationDisplay.from_predictions" title="sklearn.calibration.CalibrationDisplay.from_predictions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CalibrationDisplay.from_predictions</span></code></a></dt><dd><p>Plot calibration curve using true and predicted labels.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.calibration.CalibrationDisplay.from_estimator" title="sklearn.calibration.CalibrationDisplay.from_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CalibrationDisplay.from_estimator</span></code></a></dt><dd><p>Plot calibration curve using an estimator and data.</p>
</dd>
</dl>
</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="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">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">calibration_curve</span><span class="p">,</span> <span class="n">CalibrationDisplay</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">random_state</span><span class="o">=</span><span class="mi">0</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_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</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">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</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">&gt;&gt;&gt; </span><span class="n">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">LogisticRegression(random_state=0)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y_prob</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">prob_true</span><span class="p">,</span> <span class="n">prob_pred</span> <span class="o">=</span> <span class="n">calibration_curve</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="p">(</span><span class="n">prob_true</span><span class="p">,</span> <span class="n">prob_pred</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">disp</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="go">&lt;...&gt;</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay.from_estimator">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_estimator</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="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></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</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">name</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">ax</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">ref_line</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/calibration.py#L1206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.from_estimator" title="Link to this definition">#</a></dt>
<dd><p>Plot calibration curve using a binary classifier and data.</p>
<p>A calibration curve, also known as a reliability diagram, uses inputs
from a binary classifier and plots the average predicted probability
for each bin against the fraction of positive classes, on the
y-axis.</p>
<p>Extra keyword arguments will be passed to
<a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="(in Matplotlib v3.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></a>.</p>
<p>Read more about calibration in the <a class="reference internal" href="../calibration.html#calibration"><span class="std std-ref">User Guide</span></a> and
more about the scikit-learn visualization API in <a class="reference internal" href="../../visualizations.html#visualizations"><span class="std std-ref">Visualizations</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
<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</span></dt><dd><p>Fitted classifier or a fitted <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>
in which the last estimator is a classifier. The classifier must
have a <a class="reference internal" href="../../glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> method.</p>
</dd>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Input values.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Binary target values.</p>
</dd>
<dt><strong>n_bins</strong><span class="classifier">int, default=5</span></dt><dd><p>Number of bins to discretize the [0, 1] interval into when
calculating the calibration curve. A bigger number requires more
data.</p>
</dd>
<dt><strong>strategy</strong><span class="classifier">{‘uniform’, ‘quantile’}, default=’uniform’</span></dt><dd><p>Strategy used to define the widths of the bins.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">'uniform'</span></code>: The bins have identical widths.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">'quantile'</span></code>: The bins have the same number of samples and depend
on predicted probabilities.</p></li>
</ul>
</dd>
<dt><strong>pos_label</strong><span class="classifier">int, float, bool or str, default=None</span></dt><dd><p>The positive class when computing the calibration curve.
By default, <code class="docutils literal notranslate"><span class="pre">estimators.classes_[1]</span></code> is considered as the
positive class.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.1.</span></p>
</div>
</dd>
<dt><strong>name</strong><span class="classifier">str, default=None</span></dt><dd><p>Name for labeling curve. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, the name of the estimator is
used.</p>
</dd>
<dt><strong>ax</strong><span class="classifier">matplotlib axes, default=None</span></dt><dd><p>Axes object to plot on. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, a new figure and axes is
created.</p>
</dd>
<dt><strong>ref_line</strong><span class="classifier">bool, default=True</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, plots a reference line representing a perfectly
calibrated classifier.</p>
</dd>
<dt><strong>**kwargs</strong><span class="classifier">dict</span></dt><dd><p>Keyword arguments to be passed to <a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="(in Matplotlib v3.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>display</strong><span class="classifier"><a class="reference internal" href="#sklearn.calibration.CalibrationDisplay" title="sklearn.calibration.CalibrationDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">CalibrationDisplay</span></code></a>.</span></dt><dd><p>Object that stores computed values.</p>
</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.CalibrationDisplay.from_predictions" title="sklearn.calibration.CalibrationDisplay.from_predictions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CalibrationDisplay.from_predictions</span></code></a></dt><dd><p>Plot calibration curve using true and predicted labels.</p>
</dd>
</dl>
</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">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">CalibrationDisplay</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">random_state</span><span class="o">=</span><span class="mi">0</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_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</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">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</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">&gt;&gt;&gt; </span><span class="n">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">LogisticRegression(random_state=0)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<figure class="align-default">
<img alt="../../_images/sklearn-calibration-CalibrationDisplay-1.png" class="plot-directive" src="../../_images/sklearn-calibration-CalibrationDisplay-1.png" />
</figure>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay.from_predictions">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_prob</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">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_label</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">name</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">ax</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">ref_line</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/calibration.py#L1330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.from_predictions" title="Link to this definition">#</a></dt>
<dd><p>Plot calibration curve using true labels and predicted probabilities.</p>
<p>Calibration curve, also known as reliability diagram, uses inputs
from a binary classifier and plots the average predicted probability
for each bin against the fraction of positive classes, on the
y-axis.</p>
<p>Extra keyword arguments will be passed to
<a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="(in Matplotlib v3.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></a>.</p>
<p>Read more about calibration in the <a class="reference internal" href="../calibration.html#calibration"><span class="std std-ref">User Guide</span></a> and
more about the scikit-learn visualization API in <a class="reference internal" href="../../visualizations.html#visualizations"><span class="std std-ref">Visualizations</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>y_true</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>True labels.</p>
</dd>
<dt><strong>y_prob</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>The predicted probabilities of the positive class.</p>
</dd>
<dt><strong>n_bins</strong><span class="classifier">int, default=5</span></dt><dd><p>Number of bins to discretize the [0, 1] interval into when
calculating the calibration curve. A bigger number requires more
data.</p>
</dd>
<dt><strong>strategy</strong><span class="classifier">{‘uniform’, ‘quantile’}, default=’uniform’</span></dt><dd><p>Strategy used to define the widths of the bins.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">'uniform'</span></code>: The bins have identical widths.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">'quantile'</span></code>: The bins have the same number of samples and depend
on predicted probabilities.</p></li>
</ul>
</dd>
<dt><strong>pos_label</strong><span class="classifier">int, float, bool or str, default=None</span></dt><dd><p>The positive class when computing the calibration curve.
By default <code class="docutils literal notranslate"><span class="pre">pos_label</span></code> is set to 1.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.1.</span></p>
</div>
</dd>
<dt><strong>name</strong><span class="classifier">str, default=None</span></dt><dd><p>Name for labeling curve.</p>
</dd>
<dt><strong>ax</strong><span class="classifier">matplotlib axes, default=None</span></dt><dd><p>Axes object to plot on. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, a new figure and axes is
created.</p>
</dd>
<dt><strong>ref_line</strong><span class="classifier">bool, default=True</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, plots a reference line representing a perfectly
calibrated classifier.</p>
</dd>
<dt><strong>**kwargs</strong><span class="classifier">dict</span></dt><dd><p>Keyword arguments to be passed to <a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="(in Matplotlib v3.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>display</strong><span class="classifier"><a class="reference internal" href="#sklearn.calibration.CalibrationDisplay" title="sklearn.calibration.CalibrationDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">CalibrationDisplay</span></code></a>.</span></dt><dd><p>Object that stores computed values.</p>
</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.CalibrationDisplay.from_estimator" title="sklearn.calibration.CalibrationDisplay.from_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CalibrationDisplay.from_estimator</span></code></a></dt><dd><p>Plot calibration curve using an estimator and data.</p>
</dd>
</dl>
</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">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">CalibrationDisplay</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">random_state</span><span class="o">=</span><span class="mi">0</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_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</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">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</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">&gt;&gt;&gt; </span><span class="n">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">LogisticRegression(random_state=0)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y_prob</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_prob</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<figure class="align-default">
<img alt="../../_images/sklearn-calibration-CalibrationDisplay-2.png" class="plot-directive" src="../../_images/sklearn-calibration-CalibrationDisplay-2.png" />
</figure>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.calibration.CalibrationDisplay.plot">
<span class="sig-name descname"><span class="pre">plot</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">ax</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">name</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">ref_line</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/calibration.py#L1152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.CalibrationDisplay.plot" title="Link to this definition">#</a></dt>
<dd><p>Plot visualization.</p>
<p>Extra keyword arguments will be passed to
<a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="(in Matplotlib v3.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>ax</strong><span class="classifier">Matplotlib Axes, default=None</span></dt><dd><p>Axes object to plot on. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, a new figure and axes is
created.</p>
</dd>
<dt><strong>name</strong><span class="classifier">str, default=None</span></dt><dd><p>Name for labeling curve. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use <code class="docutils literal notranslate"><span class="pre">estimator_name</span></code> if
not <code class="docutils literal notranslate"><span class="pre">None</span></code>, otherwise no labeling is shown.</p>
</dd>
<dt><strong>ref_line</strong><span class="classifier">bool, default=True</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, plots a reference line representing a perfectly
calibrated classifier.</p>
</dd>
<dt><strong>**kwargs</strong><span class="classifier">dict</span></dt><dd><p>Keyword arguments to be passed to <a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="(in Matplotlib v3.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">matplotlib.pyplot.plot</span></code></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>display</strong><span class="classifier"><a class="reference internal" href="#sklearn.calibration.CalibrationDisplay" title="sklearn.calibration.CalibrationDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">CalibrationDisplay</span></code></a></span></dt><dd><p>Object that stores computed values.</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="When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence on the prediction. This example demonstrates how to visualize how well calibrated the predicted probabilities are using calibration curves, also known as reliability diagrams. Calibration of an uncalibrated classifier will also be demonstrated."><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="Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that for the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class."><img alt="" src="../../_images/sphx_glr_plot_compare_calibration_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a></p>
  <div class="sphx-glr-thumbnail-title">Comparison of Calibration of Classifiers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of sklearn.inspection.DecisionBoundaryDisplay to plot the predicted class probabilities of various classifiers in a 2D feature space, mostly for didactic purposes."><img alt="" src="../../_images/sphx_glr_plot_classification_probability_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py"><span class="std std-ref">Plot classification probability</span></a></p>
  <div class="sphx-glr-thumbnail-title">Plot classification probability</div>
</div></div></section>
</section>


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