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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.calibration</span></code>.calibration_curve</a><ul>
<li><a class="reference internal" href="#sklearn.calibration.calibration_curve"><code class="docutils literal notranslate"><span class="pre">calibration_curve</span></code></a></li>
</ul>
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<section id="sklearn-calibration-calibration-curve">
<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>.calibration_curve<a class="headerlink" href="#sklearn-calibration-calibration-curve" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.calibration.calibration_curve">
<span class="sig-prename descclassname"><span class="pre">sklearn.calibration.</span></span><span class="sig-name descname"><span class="pre">calibration_curve</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">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">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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/calibration.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.calibration.calibration_curve" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute true and predicted probabilities for a calibration curve.</p>
<p>The method assumes the inputs come from a binary classifier, and
discretize the [0, 1] interval into bins.</p>
<p>Calibration curves may also be referred to as reliability diagrams.</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>y_true</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>True targets.</p>
</dd>
<dt><strong>y_prob</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Probabilities of the positive class.</p>
</dd>
<dt><strong>pos_label</strong><span class="classifier">int, float, bool or str, default=None</span></dt><dd><p>The label of the positive class.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.1.</span></p>
</div>
</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. A bigger number
requires more data. Bins with no samples (i.e. without
corresponding values in <code class="docutils literal notranslate"><span class="pre">y_prob</span></code>) will not be returned, thus the
returned arrays may have less than <code class="docutils literal notranslate"><span class="pre">n_bins</span></code> values.</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>
<dl class="simple">
<dt>uniform</dt><dd><p>The bins have identical widths.</p>
</dd>
<dt>quantile</dt><dd><p>The bins have the same number of samples and depend on <code class="docutils literal notranslate"><span class="pre">y_prob</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>prob_true</strong><span class="classifier">ndarray of shape (n_bins,) or smaller</span></dt><dd><p>The proportion of samples whose class is the positive class, in each
bin (fraction of positives).</p>
</dd>
<dt><strong>prob_pred</strong><span class="classifier">ndarray of shape (n_bins,) or smaller</span></dt><dd><p>The mean predicted probability in each bin.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<p>Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good
Probabilities With Supervised Learning, in Proceedings of the 22nd
International Conference on Machine Learning (ICML).
See section 4 (Qualitative Analysis of Predictions).</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">calibration_curve</span>
<span class="gp">>>> </span><span class="n">y_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.65</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.</span><span class="p">])</span>
<span class="gp">>>> </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_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">prob_true</span>
<span class="go">array([0. , 0.5, 1. ])</span>
<span class="gp">>>> </span><span class="n">prob_pred</span>
<span class="go">array([0.2 , 0.525, 0.85 ])</span>
</pre></div>
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