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<section id="ensemble-methods">
<span id="ensemble-examples"></span><span id="sphx-glr-auto-examples-ensemble"></span><h1>Ensemble methods<a class="headerlink" href="#ensemble-methods" title="Permalink to this heading">¶</a></h1>
<p>Examples concerning the <a class="reference internal" href="../../modules/classes.html#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the training times and prediction performances of HistGradient..."><img alt="Categorical Feature Support in Gradient Boosting" src="../../_images/sphx_glr_plot_gradient_boosting_categorical_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Categorical Feature Support in Gradient Boosting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Stacking refers to a method to blend estimators. In this strategy, some estimators are individu..."><img alt="Combine predictors using stacking" src="../../_images/sphx_glr_plot_stack_predictors_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Combine predictors using stacking</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example to compare multi-output regression with random forest and the multiclass meta-estima..."><img alt="Comparing random forests and the multi-output meta estimator" src="../../_images/sphx_glr_plot_random_forest_regression_multioutput_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Comparing random forests and the multi-output meta estimator</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D sinusoidal dataset with..."><img alt="Decision Tree Regression with AdaBoost" src="../../_images/sphx_glr_plot_adaboost_regression_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Decision Tree Regression with AdaBoost</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This notebook is based on Figure 10.2 from Hastie et al 2009 [1]_ and illustrates the differenc..."><img alt="Discrete versus Real AdaBoost" src="../../_images/sphx_glr_plot_adaboost_hastie_10_2_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Discrete versus Real AdaBoost</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient boosting is an ensembling technique where several weak learners (regression trees) are..."><img alt="Early stopping of Gradient Boosting" src="../../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" />
<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 of Gradient Boosting</span></a></p>
<div class="sphx-glr-thumbnail-title">Early stopping of Gradient Boosting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of a forest of trees to evaluate the importance of features on an ar..."><img alt="Feature importances with a forest of trees" src="../../_images/sphx_glr_plot_forest_importances_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Feature importances with a forest of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on t..."><img alt="Feature transformations with ensembles of trees" src="../../_images/sphx_glr_plot_feature_transformation_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Feature transformations with ensembles of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient Boosting Out-of-Bag estimates"><img alt="Gradient Boosting Out-of-Bag estimates" src="../../_images/sphx_glr_plot_gradient_boosting_oob_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting Out-of-Bag estimates</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of w..."><img alt="Gradient Boosting regression" src="../../_images/sphx_glr_plot_gradient_boosting_regression_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Illustration of the effect of different regularization strategies for Gradient Boosting. The ex..."><img alt="Gradient Boosting regularization" src="../../_images/sphx_glr_plot_gradient_boosting_regularization_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Gradient Boosting regularization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representati..."><img alt="Hashing feature transformation using Totally Random Trees" src="../../_images/sphx_glr_plot_random_forest_embedding_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Hashing feature transformation using Totally Random Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example using IsolationForest for anomaly detection."><img alt="IsolationForest example" src="../../_images/sphx_glr_plot_isolation_forest_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">IsolationForest example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of monotonic constraints on a gradient boosting estimator."><img alt="Monotonic Constraints" src="../../_images/sphx_glr_plot_monotonic_constraints_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Monotonic Constraints</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example reproduces Figure 1 of Zhu et al [1]_ and shows how boosting can improve predictio..."><img alt="Multi-class AdaBoosted Decision Trees" src="../../_images/sphx_glr_plot_adaboost_multiclass_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Multi-class AdaBoosted Decision Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The RandomForestClassifier is trained using *bootstrap aggregation*, where each new tree is fit..."><img alt="OOB Errors for Random Forests" src="../../_images/sphx_glr_plot_ensemble_oob_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">OOB Errors for Random Forests</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of a forest of trees to evaluate the impurity based importance of th..."><img alt="Pixel importances with a parallel forest of trees" src="../../_images/sphx_glr_plot_forest_importances_faces_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Pixel importances with a parallel forest of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the class probabilities of the first sample in a toy dataset predicted by three different ..."><img alt="Plot class probabilities calculated by the VotingClassifier" src="../../_images/sphx_glr_plot_voting_probas_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Plot class probabilities calculated by the VotingClassifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the..."><img alt="Plot individual and voting regression predictions" src="../../_images/sphx_glr_plot_voting_regressor_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Plot individual and voting regression predictions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset."><img alt="Plot the decision boundaries of a VotingClassifier" src="../../_images/sphx_glr_plot_voting_decision_regions_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Plot the decision boundaries of a VotingClassifier</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the i..."><img alt="Plot the decision surfaces of ensembles of trees on the iris dataset" src="../../_images/sphx_glr_plot_forest_iris_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how quantile regression can be used to create prediction intervals."><img alt="Prediction Intervals for Gradient Boosting Regression" src="../../_images/sphx_glr_plot_gradient_boosting_quantile_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Prediction Intervals for Gradient Boosting Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares the bias-variance decomposition of the expected mean squa..."><img alt="Single estimator versus bagging: bias-variance decomposition" src="../../_images/sphx_glr_plot_bias_variance_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Single estimator versus bagging: bias-variance decomposition</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example fits an AdaBoosted decision stump on a non-linearly separable classification datas..."><img alt="Two-class AdaBoost" src="../../_images/sphx_glr_plot_adaboost_twoclass_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Two-class AdaBoost</div>
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