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<li><a class="reference internal" href="#">Gradient Boosting regression</a><ul>
<li><a class="reference internal" href="#load-the-data">Load the data</a></li>
<li><a class="reference internal" href="#data-preprocessing">Data preprocessing</a></li>
<li><a class="reference internal" href="#fit-regression-model">Fit regression model</a></li>
<li><a class="reference internal" href="#plot-training-deviance">Plot training deviance</a></li>
<li><a class="reference internal" href="#plot-feature-importance">Plot feature importance</a></li>
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<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-ensemble-plot-gradient-boosting-regression-py"><span class="std std-ref">here</span></a>
to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="gradient-boosting-regression">
<span id="sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py"></span><h1>Gradient Boosting regression<a class="headerlink" href="#gradient-boosting-regression" title="Permalink to this heading">¶</a></h1>
<p>This example demonstrates Gradient Boosting to produce a predictive
model from an ensemble of weak predictive models. Gradient boosting can be used
for regression and classification problems. Here, we will train a model to
tackle a diabetes regression task. We will obtain the results from
<a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingRegressor</span></code></a> with least squares loss
and 500 regression trees of depth 4.</p>
<p>Note: For larger datasets (n_samples >= 10000), please refer to
<a class="reference internal" href="../../modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Peter Prettenhofer <[email protected]></span>
<span class="c1"># Maria Telenczuk <https://github.com/maikia></span>
<span class="c1"># Katrina Ni <https://github.com/nilichen></span>
<span class="c1">#</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">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">ensemble</span>
<span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.inspection.permutation_importance.html#sklearn.inspection.permutation_importance" title="sklearn.inspection.permutation_importance" class="sphx-glr-backref-module-sklearn-inspection sphx-glr-backref-type-py-function"><span class="n">permutation_importance</span></a>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" title="sklearn.metrics.mean_squared_error" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">mean_squared_error</span></a>
<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>
</pre></div>
</div>
<section id="load-the-data">
<h2>Load the data<a class="headerlink" href="#load-the-data" title="Permalink to this heading">¶</a></h2>
<p>First we need to load the data.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">diabetes</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes" title="sklearn.datasets.load_diabetes" 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_diabetes</span></a><span class="p">()</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">diabetes</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">diabetes</span><span class="o">.</span><span class="n">target</span>
</pre></div>
</div>
</section>
<section id="data-preprocessing">
<h2>Data preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this heading">¶</a></h2>
<p>Next, we will split our dataset to use 90% for training and leave the rest
for testing. We will also set the regression model parameters. You can play
with these parameters to see how the results change.</p>
<p><code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> : the number of boosting stages that will be performed.
Later, we will plot deviance against boosting iterations.</p>
<p><code class="docutils literal notranslate"><span class="pre">max_depth</span></code> : limits the number of nodes in the tree.
The best value depends on the interaction of the input variables.</p>
<p><code class="docutils literal notranslate"><span class="pre">min_samples_split</span></code> : the minimum number of samples required to split an
internal node.</p>
<p><code class="docutils literal notranslate"><span class="pre">learning_rate</span></code> : how much the contribution of each tree will shrink.</p>
<p><code class="docutils literal notranslate"><span class="pre">loss</span></code> : loss function to optimize. The least squares function is used in
this case however, there are many other options (see
<a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingRegressor</span></code></a> ).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">13</span>
<span class="p">)</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"n_estimators"</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span>
<span class="s2">"max_depth"</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
<span class="s2">"min_samples_split"</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span>
<span class="s2">"learning_rate"</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="s2">"loss"</span><span class="p">:</span> <span class="s2">"squared_error"</span><span class="p">,</span>
<span class="p">}</span>
</pre></div>
</div>
</section>
<section id="fit-regression-model">
<h2>Fit regression model<a class="headerlink" href="#fit-regression-model" title="Permalink to this heading">¶</a></h2>
<p>Now we will initiate the gradient boosting regressors and fit it with our
training data. Let’s also look and the mean squared error on the test data.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">reg</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ensemble</span><span class="o">.</span><span class="n">GradientBoostingRegressor</span></a><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">reg</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">mse</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" title="sklearn.metrics.mean_squared_error" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">mean_squared_error</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"The mean squared error (MSE) on test set: </span><span class="si">{:.4f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mse</span><span class="p">))</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>The mean squared error (MSE) on test set: 3025.7877
</pre></div>
</div>
</section>
<section id="plot-training-deviance">
<h2>Plot training deviance<a class="headerlink" href="#plot-training-deviance" title="Permalink to this heading">¶</a></h2>
<p>Finally, we will visualize the results. To do that we will first compute the
test set deviance and then plot it against boosting iterations.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">test_score</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">((</span><span class="n">params</span><span class="p">[</span><span class="s2">"n_estimators"</span><span class="p">],),</span> <span class="n">dtype</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">np</span><span class="o">.</span><span class="n">float64</span></a><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">y_pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">staged_predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)):</span>
<span class="n">test_score</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" title="sklearn.metrics.mean_squared_error" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">mean_squared_error</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="n">fig</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">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</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="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>
<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">"Deviance"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" 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">plot</span></a><span class="p">(</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">params</span><span class="p">[</span><span class="s2">"n_estimators"</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">reg</span><span class="o">.</span><span class="n">train_score_</span><span class="p">,</span>
<span class="s2">"b-"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"Training Set Deviance"</span><span class="p">,</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" 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">plot</span></a><span class="p">(</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">params</span><span class="p">[</span><span class="s2">"n_estimators"</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">test_score</span><span class="p">,</span> <span class="s2">"r-"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Test Set Deviance"</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">loc</span><span class="o">=</span><span class="s2">"upper right"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" 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">xlabel</span></a><span class="p">(</span><span class="s2">"Boosting Iterations"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" 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">ylabel</span></a><span class="p">(</span><span class="s2">"Deviance"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</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>
<img src="../../_images/sphx_glr_plot_gradient_boosting_regression_001.png" srcset="../../_images/sphx_glr_plot_gradient_boosting_regression_001.png" alt="Deviance" class = "sphx-glr-single-img"/></section>
<section id="plot-feature-importance">
<h2>Plot feature importance<a class="headerlink" href="#plot-feature-importance" title="Permalink to this heading">¶</a></h2>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Careful, impurity-based feature importances can be misleading for
<strong>high cardinality</strong> features (many unique values). As an alternative,
the permutation importances of <code class="docutils literal notranslate"><span class="pre">reg</span></code> can be computed on a
held out test set. See <a class="reference internal" href="../../modules/permutation_importance.html#permutation-importance"><span class="std std-ref">Permutation feature importance</span></a> for more details.</p>
</div>
<p>For this example, the impurity-based and permutation methods identify the
same 2 strongly predictive features but not in the same order. The third most
predictive feature, “bp”, is also the same for the 2 methods. The remaining
features are less predictive and the error bars of the permutation plot
show that they overlap with 0.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">feature_importance</span> <span class="o">=</span> <span class="n">reg</span><span class="o">.</span><span class="n">feature_importances_</span>
<span class="n">sorted_idx</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.argsort.html#numpy.argsort" title="numpy.argsort" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">argsort</span></a><span class="p">(</span><span class="n">feature_importance</span><span class="p">)</span>
<span class="n">pos</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">sorted_idx</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="mf">0.5</span>
<span class="n">fig</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">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</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="mi">1</span><span class="p">,</span> <span class="mi">2</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.barh.html#matplotlib.pyplot.barh" title="matplotlib.pyplot.barh" 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">barh</span></a><span class="p">(</span><span class="n">pos</span><span class="p">,</span> <span class="n">feature_importance</span><span class="p">[</span><span class="n">sorted_idx</span><span class="p">],</span> <span class="n">align</span><span class="o">=</span><span class="s2">"center"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" 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">yticks</span></a><span class="p">(</span><span class="n">pos</span><span class="p">,</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="n">diabetes</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)[</span><span class="n">sorted_idx</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">"Feature Importance (MDI)"</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.inspection.permutation_importance.html#sklearn.inspection.permutation_importance" title="sklearn.inspection.permutation_importance" class="sphx-glr-backref-module-sklearn-inspection sphx-glr-backref-type-py-function"><span class="n">permutation_importance</span></a><span class="p">(</span>
<span class="n">reg</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">n_repeats</span><span class="o">=</span><span class="mi">10</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">n_jobs</span><span class="o">=</span><span class="mi">2</span>
<span class="p">)</span>
<span class="n">sorted_idx</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">importances_mean</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</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="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>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html#matplotlib.pyplot.boxplot" title="matplotlib.pyplot.boxplot" 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">boxplot</span></a><span class="p">(</span>
<span class="n">result</span><span class="o">.</span><span class="n">importances</span><span class="p">[</span><span class="n">sorted_idx</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
<span class="n">vert</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">labels</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="n">diabetes</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)[</span><span class="n">sorted_idx</span><span class="p">],</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">"Permutation Importance (test set)"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</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>
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if (jthis.find('.gp').length > 0) {
var button = $('<span class="copybutton">>>></span>');
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jthis.prepend(button);
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button.attr('title', hide_text);
button.data('hidden', 'false');
}
});
/*** Add permalink buttons next to glossary terms ***/
$('dl.glossary > dt[id]').append(function() {
return ('<a class="headerlink" href="#' +
this.getAttribute('id') +
'" title="Permalink to this term">¶</a>');
});
});
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