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<li><a class="reference internal" href="#">Confusion matrix</a></li>
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<section class="sphx-glr-example-title" id="confusion-matrix">
<span id="sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py"></span><h1>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Link to this heading">¶</a></h1>
<p>Example of confusion matrix usage to evaluate the quality
of the output of a classifier on the iris data set. The
diagonal elements represent the number of points for which
the predicted label is equal to the true label, while
off-diagonal elements are those that are mislabeled by the
classifier. The higher the diagonal values of the confusion
matrix the better, indicating many correct predictions.</p>
<p>The figures show the confusion matrix with and without
normalization by class support size (number of elements
in each class). This kind of normalization can be
interesting in case of class imbalance to have a more
visual interpretation of which class is being misclassified.</p>
<p>Here the results are not as good as they could be as our
choice for the regularization parameter C was not the best.
In real life applications this parameter is usually chosen
using <a class="reference internal" href="../../modules/grid_search.html#grid-search"><span class="std std-ref">Tuning the hyper-parameters of an estimator</span></a>.</p>
<ul class="sphx-glr-horizontal">
<li><img src="../../_images/sphx_glr_plot_confusion_matrix_001.png" srcset="../../_images/sphx_glr_plot_confusion_matrix_001.png" alt="Confusion matrix, without normalization" class = "sphx-glr-multi-img"/></li>
<li><img src="../../_images/sphx_glr_plot_confusion_matrix_002.png" srcset="../../_images/sphx_glr_plot_confusion_matrix_002.png" alt="Normalized confusion matrix" class = "sphx-glr-multi-img"/></li>
</ul>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Confusion matrix, without normalization
[[13 0 0]
[ 0 10 6]
[ 0 0 9]]
Normalized confusion matrix
[[1. 0. 0. ]
[0. 0.62 0.38]
[0. 0. 1. ]]
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">svm</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">ConfusionMatrixDisplay</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
<span class="c1"># import some data to play with</span>
<span class="n">iris</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris" title="sklearn.datasets.load_iris" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span></a><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>
<span class="n">class_names</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target_names</span>
<span class="c1"># Split the data into a training set and a test set</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> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</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="c1"># Run classifier, using a model that is too regularized (C too low) to see</span>
<span class="c1"># the impact on the results</span>
<span class="n">classifier</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</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>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.set_printoptions.html#numpy.set_printoptions" title="numpy.set_printoptions" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span></a><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># Plot non-normalized confusion matrix</span>
<span class="n">titles_options</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="s2">"Confusion matrix, without normalization"</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"Normalized confusion matrix"</span><span class="p">,</span> <span class="s2">"true"</span><span class="p">),</span>
<span class="p">]</span>
<span class="k">for</span> <span class="n">title</span><span class="p">,</span> <span class="n">normalize</span> <span class="ow">in</span> <span class="n">titles_options</span><span class="p">:</span>
<span class="n">disp</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay.from_estimator" title="sklearn.metrics.ConfusionMatrixDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-metrics-ConfusionMatrixDisplay sphx-glr-backref-type-py-method"><span class="n">ConfusionMatrixDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
<span class="n">classifier</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="n">display_labels</span><span class="o">=</span><span class="n">class_names</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Blues</span><span class="p">,</span>
<span class="n">normalize</span><span class="o">=</span><span class="n">normalize</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">disp</span><span class="o">.</span><span class="n">ax_</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">disp</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.164 seconds)</p>
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