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  <section id="computation-times">
<span id="sphx-glr-auto-examples-ensemble-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
<p><strong>02:35.215</strong> total execution time for 24 files <strong>from auto_examples/ensemble</strong>:</p>
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<tr class="row-odd"><th class="head"><p>Example</p></th>
<th class="head"><p>Time</p></th>
<th class="head"><p>Mem (MB)</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="plot_forest_hist_grad_boosting_comparison.html#sphx-glr-auto-examples-ensemble-plot-forest-hist-grad-boosting-comparison-py"><span class="std std-ref">Comparing Random Forests and Histogram Gradient Boosting models</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_forest_hist_grad_boosting_comparison.py</span></code>)</p></td>
<td><p>00:54.562</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py"><span class="std std-ref">Combine predictors using stacking</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_stack_predictors.py</span></code>)</p></td>
<td><p>00:29.296</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_gradient_boosting_categorical.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-categorical-py"><span class="std std-ref">Categorical Feature Support in Gradient Boosting</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gradient_boosting_categorical.py</span></code>)</p></td>
<td><p>00:14.872</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py"><span class="std std-ref">Gradient Boosting Out-of-Bag estimates</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gradient_boosting_oob.py</span></code>)</p></td>
<td><p>00:09.296</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py"><span class="std std-ref">Prediction Intervals for Gradient Boosting Regression</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gradient_boosting_quantile.py</span></code>)</p></td>
<td><p>00:09.113</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py"><span class="std std-ref">Gradient Boosting regularization</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gradient_boosting_regularization.py</span></code>)</p></td>
<td><p>00:07.974</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_forest_iris.py</span></code>)</p></td>
<td><p>00:06.537</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_adaboost_multiclass.py</span></code>)</p></td>
<td><p>00:04.342</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py"><span class="std std-ref">OOB Errors for Random Forests</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ensemble_oob.py</span></code>)</p></td>
<td><p>00:03.634</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">Early stopping in Gradient Boosting</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gradient_boosting_early_stopping.py</span></code>)</p></td>
<td><p>00:03.297</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_feature_transformation.py</span></code>)</p></td>
<td><p>00:02.692</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py"><span class="std std-ref">Gradient Boosting regression</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_gradient_boosting_regression.py</span></code>)</p></td>
<td><p>00:01.364</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_forest_importances_faces.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-faces-py"><span class="std std-ref">Pixel importances with a parallel forest of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_forest_importances_faces.py</span></code>)</p></td>
<td><p>00:01.200</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_bias_variance.html#sphx-glr-auto-examples-ensemble-plot-bias-variance-py"><span class="std std-ref">Single estimator versus bagging: bias-variance decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bias_variance.py</span></code>)</p></td>
<td><p>00:01.141</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_voting_regressor.py</span></code>)</p></td>
<td><p>00:00.973</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py"><span class="std std-ref">Feature importances with a forest of trees</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_forest_importances.py</span></code>)</p></td>
<td><p>00:00.907</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py"><span class="std std-ref">Plot the decision boundaries of a VotingClassifier</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_voting_decision_regions.py</span></code>)</p></td>
<td><p>00:00.680</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_adaboost_twoclass.py</span></code>)</p></td>
<td><p>00:00.664</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_monotonic_constraints.html#sphx-glr-auto-examples-ensemble-plot-monotonic-constraints-py"><span class="std std-ref">Monotonic Constraints</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_monotonic_constraints.py</span></code>)</p></td>
<td><p>00:00.581</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py"><span class="std std-ref">Comparing random forests and the multi-output meta estimator</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_random_forest_regression_multioutput.py</span></code>)</p></td>
<td><p>00:00.515</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Decision Tree Regression with AdaBoost</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_adaboost_regression.py</span></code>)</p></td>
<td><p>00:00.450</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_isolation_forest.py</span></code>)</p></td>
<td><p>00:00.436</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_random_forest_embedding.html#sphx-glr-auto-examples-ensemble-plot-random-forest-embedding-py"><span class="std std-ref">Hashing feature transformation using Totally Random Trees</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_random_forest_embedding.py</span></code>)</p></td>
<td><p>00:00.362</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py"><span class="std std-ref">Plot class probabilities calculated by the VotingClassifier</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_voting_probas.py</span></code>)</p></td>
<td><p>00:00.330</p></td>
<td><p>0.0</p></td>
</tr>
</tbody>
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