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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.calibration</span></code>.calibration_curve</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-calibration-calibration-curve">Examples using <code class="docutils literal"><span class="pre">sklearn.calibration.calibration_curve</span></code></a></li>
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<div class="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"><span class="pre">sklearn.calibration</span></code></a>.calibration_curve<a class="headerlink" href="#sklearn-calibration-calibration-curve" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.calibration.calibration_curve">
<code class="descclassname">sklearn.calibration.</code><code class="descname">calibration_curve</code><span class="sig-paren">(</span><em>y_true</em>, <em>y_prob</em>, <em>normalize=False</em>, <em>n_bins=5</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/calibration.py#L517"><span class="viewcode-link">[source]</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>
<blockquote>
<div><p>The method assumes the inputs come from a binary classifier.</p>
<p>Calibration curves may also be referred to as reliability diagrams.</p>
</div></blockquote>
<p>Read more in the <a class="reference internal" href="../calibration.html#calibration"><span class="std std-ref">User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>y_true</strong> : array, shape (n_samples,)</p>
<blockquote>
<div><p>True targets.</p>
</div></blockquote>
<p><strong>y_prob</strong> : array, shape (n_samples,)</p>
<blockquote>
<div><p>Probabilities of the positive class.</p>
</div></blockquote>
<p><strong>normalize</strong> : bool, optional, default=False</p>
<blockquote>
<div><p>Whether y_prob needs to be normalized into the bin [0, 1], i.e. is not
a proper probability. If True, the smallest value in y_prob is mapped
onto 0 and the largest one onto 1.</p>
</div></blockquote>
<p><strong>n_bins</strong> : int</p>
<blockquote>
<div><p>Number of bins. A bigger number requires more data.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>prob_true</strong> : array, shape (n_bins,)</p>
<blockquote>
<div><p>The true probability in each bin (fraction of positives).</p>
</div></blockquote>
<p><strong>prob_pred</strong> : array, shape (n_bins,)</p>
<blockquote class="last">
<div><p>The mean predicted probability in each bin.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<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>
</dd></dl>
<div class="section" id="examples-using-sklearn-calibration-calibration-curve">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.calibration.calibration_curve</span></code><a class="headerlink" href="#examples-using-sklearn-calibration-calibration-curve" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Well calibrated classifiers are probabilistic classifiers for which the output of the predict_p..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_compare_calibration_thumb.png" src="../../_images/sphx_glr_plot_compare_calibration_thumb.png" />
<p class="caption"><span class="caption-text"><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></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification one often wants to predict not only the class label, but also th..."><div class="figure" id="id2">
<img alt="../../_images/sphx_glr_plot_calibration_curve_thumb.png" src="../../_images/sphx_glr_plot_calibration_curve_thumb.png" />
<p class="caption"><span class="caption-text"><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></span></p>
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