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<section class="sphx-glr-example-title" id="classifier-comparison">
<span id="sphx-glr-auto-examples-classification-plot-classifier-comparison-py"></span><h1>Classifier comparison<a class="headerlink" href="#classifier-comparison" title="Link to this heading">¶</a></h1>
<p>A comparison of several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.
This should be taken with a grain of salt, as the intuition conveyed by
these examples does not necessarily carry over to real datasets.</p>
<p>Particularly in high-dimensional spaces, data can more easily be separated
linearly and the simplicity of classifiers such as naive Bayes and linear SVMs
might lead to better generalization than is achieved by other classifiers.</p>
<p>The plots show training points in solid colors and testing points
semi-transparent. The lower right shows the classification accuracy on the test
set.</p>
<img src="../../_images/sphx_glr_plot_classifier_comparison_001.png" srcset="../../_images/sphx_glr_plot_classifier_comparison_001.png" alt="Input data, Nearest Neighbors, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest, Neural Net, AdaBoost, Naive Bayes, QDA" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Code source: Gaël Varoquaux</span>
<span class="c1"># Andreas Müller</span>
<span class="c1"># Modified for documentation by Jaques Grobler</span>
<span class="c1"># License: BSD 3 clause</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">matplotlib.colors</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.ListedColormap.html#matplotlib.colors.ListedColormap" title="matplotlib.colors.ListedColormap" class="sphx-glr-backref-module-matplotlib-colors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ListedColormap</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_circles</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_moons</span></a>
<span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">QuadraticDiscriminantAnalysis</span></a>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AdaBoostClassifier</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
<span class="kn">from</span> <span class="nn">sklearn.gaussian_process</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier" class="sphx-glr-backref-module-sklearn-gaussian_process sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianProcessClassifier</span></a>
<span class="kn">from</span> <span class="nn">sklearn.gaussian_process.kernels</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.gaussian_process.kernels.RBF.html#sklearn.gaussian_process.kernels.RBF" title="sklearn.gaussian_process.kernels.RBF" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RBF</span></a>
<span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">DecisionBoundaryDisplay</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="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB" class="sphx-glr-backref-module-sklearn-naive_bayes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianNB</span></a>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier" class="sphx-glr-backref-module-sklearn-neighbors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNeighborsClassifier</span></a>
<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network.MLPClassifier" class="sphx-glr-backref-module-sklearn-neural_network sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MLPClassifier</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</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">SVC</span></a>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeClassifier</span></a>
<span class="n">names</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"Nearest Neighbors"</span><span class="p">,</span>
<span class="s2">"Linear SVM"</span><span class="p">,</span>
<span class="s2">"RBF SVM"</span><span class="p">,</span>
<span class="s2">"Gaussian Process"</span><span class="p">,</span>
<span class="s2">"Decision Tree"</span><span class="p">,</span>
<span class="s2">"Random Forest"</span><span class="p">,</span>
<span class="s2">"Neural Net"</span><span class="p">,</span>
<span class="s2">"AdaBoost"</span><span class="p">,</span>
<span class="s2">"Naive Bayes"</span><span class="p">,</span>
<span class="s2">"QDA"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">classifiers</span> <span class="o">=</span> <span class="p">[</span>
<a href="../../modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier" class="sphx-glr-backref-module-sklearn-neighbors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KNeighborsClassifier</span></a><span class="p">(</span><span class="mi">3</span><span class="p">),</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">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">0.025</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</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">SVC</span></a><span class="p">(</span><span class="n">gamma</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier" class="sphx-glr-backref-module-sklearn-gaussian_process sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianProcessClassifier</span></a><span class="p">(</span><span class="mf">1.0</span> <span class="o">*</span> <a href="../../modules/generated/sklearn.gaussian_process.kernels.RBF.html#sklearn.gaussian_process.kernels.RBF" title="sklearn.gaussian_process.kernels.RBF" class="sphx-glr-backref-module-sklearn-gaussian_process-kernels sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RBF</span></a><span class="p">(</span><span class="mf">1.0</span><span class="p">),</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeClassifier</span></a><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">(</span>
<span class="n">max_depth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
<span class="p">),</span>
<a href="../../modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier" title="sklearn.neural_network.MLPClassifier" class="sphx-glr-backref-module-sklearn-neural_network sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MLPClassifier</span></a><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AdaBoostClassifier</span></a><span class="p">(</span><span class="n">algorithm</span><span class="o">=</span><span class="s2">"SAMME"</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB" class="sphx-glr-backref-module-sklearn-naive_bayes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GaussianNB</span></a><span class="p">(),</span>
<a href="../../modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis" class="sphx-glr-backref-module-sklearn-discriminant_analysis sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">QuadraticDiscriminantAnalysis</span></a><span class="p">(),</span>
<span class="p">]</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a><span class="p">(</span>
<span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_clusters_per_class</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span>
<span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">X</span> <span class="o">+=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">linearly_separable</span> <span class="o">=</span> <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">datasets</span> <span class="o">=</span> <span class="p">[</span>
<a href="../../modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_moons</span></a><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_circles</span></a><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">linearly_separable</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">figure</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" 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">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">27</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># iterate over datasets</span>
<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
<span class="c1"># preprocess dataset, split into training and test part</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ds</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">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
<span class="p">)</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
<span class="c1"># just plot the dataset first</span>
<span class="n">cm</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">RdBu</span>
<span class="n">cm_bright</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.ListedColormap.html#matplotlib.colors.ListedColormap" title="matplotlib.colors.ListedColormap" class="sphx-glr-backref-module-matplotlib-colors sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ListedColormap</span></a><span class="p">([</span><span class="s2">"#FF0000"</span><span class="p">,</span> <span class="s2">"#0000FF"</span><span class="p">])</span>
<span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" 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">subplot</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">classifiers</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Input data"</span><span class="p">)</span>
<span class="c1"># Plot the training points</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="c1"># Plot the testing points</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">X_test</span><span class="p">[:,</span> <span class="mi">0</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="n">c</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">"k"</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># iterate over classifiers</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">):</span>
<span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" 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">subplot</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">classifiers</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">StandardScaler</span></a><span class="p">(),</span> <span class="n">clf</span><span class="p">)</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="n">score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</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>
<a href="../../modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html#sklearn.inspection.DecisionBoundaryDisplay.from_estimator" title="sklearn.inspection.DecisionBoundaryDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-inspection-DecisionBoundaryDisplay sphx-glr-backref-type-py-method"><span class="n">DecisionBoundaryDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
<span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.5</span>
<span class="p">)</span>
<span class="c1"># Plot the training points</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">"k"</span>
<span class="p">)</span>
<span class="c1"># Plot the testing points</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
<span class="n">X_test</span><span class="p">[:,</span> <span class="mi">0</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="n">c</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span>
<span class="n">edgecolors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">)</span>
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<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
<span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
<span class="n">x_max</span> <span class="o">-</span> <span class="mf">0.3</span><span class="p">,</span>
<span class="n">y_min</span> <span class="o">+</span> <span class="mf">0.3</span><span class="p">,</span>
<span class="p">(</span><span class="s2">"</span><span class="si">%.2f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">"0"</span><span class="p">),</span>
<span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
<span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">"right"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" 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">tight_layout</span></a><span class="p">()</span>
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