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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code>.confusion_matrix</a><ul>
<li><a class="reference internal" href="#sklearn.metrics.confusion_matrix"><code class="docutils literal notranslate"><span class="pre">confusion_matrix</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-metrics-confusion-matrix">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.metrics.confusion_matrix</span></code></a></li>
</ul>
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  <section id="sklearn-metrics-confusion-matrix">
<h1><a class="reference internal" href="../classes.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a>.confusion_matrix<a class="headerlink" href="#sklearn-metrics-confusion-matrix" title="Link to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.metrics.confusion_matrix">
<span class="sig-prename descclassname"><span class="pre">sklearn.metrics.</span></span><span class="sig-name descname"><span class="pre">confusion_matrix</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_pred</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">labels</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">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="n"><span class="pre">normalize</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/0b0bddda2/sklearn/metrics/_classification.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.metrics.confusion_matrix" title="Link to this definition">¶</a></dt>
<dd><p>Compute confusion matrix to evaluate the accuracy of a classification.</p>
<p>By definition a confusion matrix <span class="math notranslate nohighlight">\(C\)</span> is such that <span class="math notranslate nohighlight">\(C_{i, j}\)</span>
is equal to the number of observations known to be in group <span class="math notranslate nohighlight">\(i\)</span> and
predicted to be in group <span class="math notranslate nohighlight">\(j\)</span>.</p>
<p>Thus in binary classification, the count of true negatives is
<span class="math notranslate nohighlight">\(C_{0,0}\)</span>, false negatives is <span class="math notranslate nohighlight">\(C_{1,0}\)</span>, true positives is
<span class="math notranslate nohighlight">\(C_{1,1}\)</span> and false positives is <span class="math notranslate nohighlight">\(C_{0,1}\)</span>.</p>
<p>Read more in the <a class="reference internal" href="../model_evaluation.html#confusion-matrix"><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>Ground truth (correct) target values.</p>
</dd>
<dt><strong>y_pred</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Estimated targets as returned by a classifier.</p>
</dd>
<dt><strong>labels</strong><span class="classifier">array-like of shape (n_classes), default=None</span></dt><dd><p>List of labels to index the matrix. This may be used to reorder
or select a subset of labels.
If <code class="docutils literal notranslate"><span class="pre">None</span></code> is given, those that appear at least once
in <code class="docutils literal notranslate"><span class="pre">y_true</span></code> or <code class="docutils literal notranslate"><span class="pre">y_pred</span></code> are used in sorted order.</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>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.18.</span></p>
</div>
</dd>
<dt><strong>normalize</strong><span class="classifier">{‘true’, ‘pred’, ‘all’}, default=None</span></dt><dd><p>Normalizes confusion matrix over the true (rows), predicted (columns)
conditions or all the population. If None, confusion matrix will not be
normalized.</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_classes, n_classes)</span></dt><dd><p>Confusion matrix whose i-th row and j-th
column entry indicates the number of
samples with true label being i-th class
and predicted label being j-th class.</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.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay.from_estimator" title="sklearn.metrics.ConfusionMatrixDisplay.from_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConfusionMatrixDisplay.from_estimator</span></code></a></dt><dd><p>Plot the confusion matrix given an estimator, the data, and the label.</p>
</dd>
<dt><a class="reference internal" href="sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay.from_predictions" title="sklearn.metrics.ConfusionMatrixDisplay.from_predictions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConfusionMatrixDisplay.from_predictions</span></code></a></dt><dd><p>Plot the confusion matrix given the true and predicted labels.</p>
</dd>
<dt><a class="reference internal" href="sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay" title="sklearn.metrics.ConfusionMatrixDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConfusionMatrixDisplay</span></code></a></dt><dd><p>Confusion Matrix visualization.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r21c75359fccc-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://en.wikipedia.org/wiki/Confusion_matrix">Wikipedia entry for the Confusion matrix</a>
(Wikipedia and other references may use a different
convention for axes).</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.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</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="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">confusion_matrix</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="go">array([[2, 0, 0],</span>
<span class="go">       [0, 0, 1],</span>
<span class="go">       [1, 0, 2]])</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;ant&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;ant&quot;</span><span class="p">,</span> <span class="s2">&quot;bird&quot;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;ant&quot;</span><span class="p">,</span> <span class="s2">&quot;ant&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">,</span> <span class="s2">&quot;ant&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">confusion_matrix</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">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ant&quot;</span><span class="p">,</span> <span class="s2">&quot;bird&quot;</span><span class="p">,</span> <span class="s2">&quot;cat&quot;</span><span class="p">])</span>
<span class="go">array([[2, 0, 0],</span>
<span class="go">       [0, 0, 1],</span>
<span class="go">       [1, 0, 2]])</span>
</pre></div>
</div>
<p>In the binary case, we can extract true positives, etc. as follows:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">=</span> <span class="n">confusion_matrix</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">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</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">0</span><span class="p">])</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="p">(</span><span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span><span class="p">)</span>
<span class="go">(0, 2, 1, 1)</span>
</pre></div>
</div>
</dd></dl>

<section id="examples-using-sklearn-metrics-confusion-matrix">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.metrics.confusion_matrix</span></code><a class="headerlink" href="#examples-using-sklearn-metrics-confusion-matrix" title="Link to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will construct display objects, ConfusionMatrixDisplay, RocCurveDisplay, an..."><img alt="" src="../../_images/sphx_glr_plot_display_object_visualization_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_display_object_visualization.html#sphx-glr-auto-examples-miscellaneous-plot-display-object-visualization-py"><span class="std std-ref">Visualizations with Display Objects</span></a></p>
  <div class="sphx-glr-thumbnail-title">Visualizations with Display Objects</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Demonstrates an active learning technique to learn handwritten digits using label propagation."><img alt="" src="../../_images/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py"><span class="std std-ref">Label Propagation digits active learning</span></a></p>
  <div class="sphx-glr-thumbnail-title">Label Propagation digits active learning</div>
</div></div><div class="clearer"></div></section>
</section>


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