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<div class="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="Permalink to this headline">¶</a></h1>
<p><strong>01:12.980</strong> total execution time for <strong>auto_examples_ensemble</strong> files:</p>
<ul class="simple">
<li><strong>00:17.246</strong>: <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 of Gradient Boosting</span></a> (<code class="docutils literal"><span class="pre">plot_gradient_boosting_early_stopping.py</span></code>)</li>
<li><strong>00:11.385</strong>: <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"><span class="pre">plot_adaboost_multiclass.py</span></code>)</li>
<li><strong>00:09.017</strong>: <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"><span class="pre">plot_gradient_boosting_regularization.py</span></code>)</li>
<li><strong>00:06.823</strong>: <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"><span class="pre">plot_forest_iris.py</span></code>)</li>
<li><strong>00:05.387</strong>: <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"><span class="pre">plot_ensemble_oob.py</span></code>)</li>
<li><strong>00:04.560</strong>: <a class="reference internal" href="plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py"><span class="std std-ref">Discrete versus Real AdaBoost</span></a> (<code class="docutils literal"><span class="pre">plot_adaboost_hastie_10_2.py</span></code>)</li>
<li><strong>00:04.529</strong>: <a class="reference internal" href="plot_partial_dependence.html#sphx-glr-auto-examples-ensemble-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence Plots</span></a> (<code class="docutils literal"><span class="pre">plot_partial_dependence.py</span></code>)</li>
<li><strong>00:03.101</strong>: <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"><span class="pre">plot_adaboost_twoclass.py</span></code>)</li>
<li><strong>00:02.613</strong>: <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"><span class="pre">plot_feature_transformation.py</span></code>)</li>
<li><strong>00:02.565</strong>: <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"><span class="pre">plot_gradient_boosting_oob.py</span></code>)</li>
<li><strong>00:01.119</strong>: <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"><span class="pre">plot_forest_importances_faces.py</span></code>)</li>
<li><strong>00:00.801</strong>: <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"><span class="pre">plot_bias_variance.py</span></code>)</li>
<li><strong>00:00.541</strong>: <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"><span class="pre">plot_random_forest_embedding.py</span></code>)</li>
<li><strong>00:00.513</strong>: <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"><span class="pre">plot_gradient_boosting_regression.py</span></code>)</li>
<li><strong>00:00.484</strong>: <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"><span class="pre">plot_gradient_boosting_quantile.py</span></code>)</li>
<li><strong>00:00.475</strong>: <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"><span class="pre">plot_voting_decision_regions.py</span></code>)</li>
<li><strong>00:00.441</strong>: <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"><span class="pre">plot_adaboost_regression.py</span></code>)</li>
<li><strong>00:00.390</strong>: <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"><span class="pre">plot_random_forest_regression_multioutput.py</span></code>)</li>
<li><strong>00:00.389</strong>: <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 forests of trees</span></a> (<code class="docutils literal"><span class="pre">plot_forest_importances.py</span></code>)</li>
<li><strong>00:00.325</strong>: <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"><span class="pre">plot_isolation_forest.py</span></code>)</li>
<li><strong>00:00.276</strong>: <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"><span class="pre">plot_voting_probas.py</span></code>)</li>
</ul>
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