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<li><a class="reference internal" href="#">Plot class probabilities calculated by the VotingClassifier</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code or to run this example in your browser via JupyterLite or Binder</p>
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<section class="sphx-glr-example-title" id="plot-class-probabilities-calculated-by-the-votingclassifier">
<span id="sphx-glr-auto-examples-ensemble-plot-voting-probas-py"></span><h1>Plot class probabilities calculated by the VotingClassifier<a class="headerlink" href="#plot-class-probabilities-calculated-by-the-votingclassifier" title="Permalink to this heading">¶</a></h1>
<p>Plot the class probabilities of the first sample in a toy dataset predicted by
three different classifiers and averaged by the
<a class="reference internal" href="../../modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a>.</p>
<p>First, three exemplary classifiers are initialized
(<a class="reference internal" href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a>, <a class="reference internal" href="../../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianNB</span></code></a>,
and <a class="reference internal" href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a>) and used to initialize a
soft-voting <a class="reference internal" href="../../modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">VotingClassifier</span></code></a> with weights <code class="docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">1,</span> <span class="pre">5]</span></code>, which
means that the predicted probabilities of the
<a class="reference internal" href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a> count 5 times as much as the weights
of the other classifiers when the averaged probability is calculated.</p>
<p>To visualize the probability weighting, we fit each classifier on the training
set and plot the predicted class probabilities for the first sample in this
example dataset.</p>
<img src="../../_images/sphx_glr_plot_voting_probas_001.png" srcset="../../_images/sphx_glr_plot_voting_probas_001.png" alt="Class probabilities for sample 1 by different classifiers" class = "sphx-glr-single-img"/><div class="highlight-default 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.ensemble</span> <span class="kn">import</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> <a href="../../modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">VotingClassifier</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</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="n">clf1</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><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">123</span><span class="p">)</span>
<span class="n">clf2</span> <span class="o">=</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">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">)</span>
<span class="n">clf3</span> <span class="o">=</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>
<span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([[</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">1.2</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.4</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">3.4</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.1</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">]])</span>
<span class="n">y</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="n">eclf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">VotingClassifier</span></a><span class="p">(</span>
<span class="n">estimators</span><span class="o">=</span><span class="p">[(</span><span class="s2">"lr"</span><span class="p">,</span> <span class="n">clf1</span><span class="p">),</span> <span class="p">(</span><span class="s2">"rf"</span><span class="p">,</span> <span class="n">clf2</span><span class="p">),</span> <span class="p">(</span><span class="s2">"gnb"</span><span class="p">,</span> <span class="n">clf3</span><span class="p">)],</span>
<span class="n">voting</span><span class="o">=</span><span class="s2">"soft"</span><span class="p">,</span>
<span class="n">weights</span><span class="o">=</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">5</span><span class="p">],</span>
<span class="p">)</span>
<span class="c1"># predict class probabilities for all classifiers</span>
<span class="n">probas</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span><span class="o">.</span><span class="n">fit</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="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="p">(</span><span class="n">clf1</span><span class="p">,</span> <span class="n">clf2</span><span class="p">,</span> <span class="n">clf3</span><span class="p">,</span> <span class="n">eclf</span><span class="p">)]</span>
<span class="c1"># get class probabilities for the first sample in the dataset</span>
<span class="n">class1_1</span> <span class="o">=</span> <span class="p">[</span><span class="n">pr</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="k">for</span> <span class="n">pr</span> <span class="ow">in</span> <span class="n">probas</span><span class="p">]</span>
<span class="n">class2_1</span> <span class="o">=</span> <span class="p">[</span><span class="n">pr</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="k">for</span> <span class="n">pr</span> <span class="ow">in</span> <span class="n">probas</span><span class="p">]</span>
<span class="c1"># plotting</span>
<span class="n">N</span> <span class="o">=</span> <span class="mi">4</span> <span class="c1"># number of groups</span>
<span class="n">ind</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="c1"># group positions</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.35</span> <span class="c1"># bar width</span>
<span class="n">fig</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.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" 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">subplots</span></a><span class="p">()</span>
<span class="c1"># bars for classifier 1-3</span>
<span class="n">p1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">(([</span><span class="n">class1_1</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]])),</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"green"</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="n">p2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span>
<span class="n">ind</span> <span class="o">+</span> <span class="n">width</span><span class="p">,</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html#numpy.hstack" title="numpy.hstack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">(([</span><span class="n">class2_1</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]])),</span>
<span class="n">width</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"lightgreen"</span><span class="p">,</span>
<span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># bars for VotingClassifier</span>
<span class="n">p3</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</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="n">class1_1</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]],</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="n">p4</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span>
<span class="n">ind</span> <span class="o">+</span> <span class="n">width</span><span class="p">,</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="n">class2_1</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]],</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"steelblue"</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span>
<span class="p">)</span>
<span class="c1"># plot annotations</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axvline.html#matplotlib.pyplot.axvline" title="matplotlib.pyplot.axvline" 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">axvline</span></a><span class="p">(</span><span class="mf">2.8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"dashed"</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">ind</span> <span class="o">+</span> <span class="n">width</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span>
<span class="p">[</span>
<span class="s2">"LogisticRegression</span><span class="se">\n</span><span class="s2">weight 1"</span><span class="p">,</span>
<span class="s2">"GaussianNB</span><span class="se">\n</span><span class="s2">weight 1"</span><span class="p">,</span>
<span class="s2">"RandomForestClassifier</span><span class="se">\n</span><span class="s2">weight 5"</span><span class="p">,</span>
<span class="s2">"VotingClassifier</span><span class="se">\n</span><span class="s2">(average probabilities)"</span><span class="p">,</span>
<span class="p">],</span>
<span class="n">rotation</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
<span class="n">ha</span><span class="o">=</span><span class="s2">"right"</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="matplotlib.pyplot.ylim" 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">ylim</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" 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">title</span></a><span class="p">(</span><span class="s2">"Class probabilities for sample 1 by different classifiers"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" 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">legend</span></a><span class="p">([</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[</span><span class="s2">"class 1"</span><span class="p">,</span> <span class="s2">"class 2"</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s2">"upper left"</span><span class="p">)</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>
<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>
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