Ensemble methods¶
Examples concerning the sklearn.ensemble
module.
![Comparing random forests and the multi-output meta estimator](../../_images/sphx_glr_plot_random_forest_regression_multioutput_thumb.png)
Comparing random forests and the multi-output meta estimator
Comparing random forests and the multi-output meta estimator
![Hashing feature transformation using Totally Random Trees](../../_images/sphx_glr_plot_random_forest_embedding_thumb.png)
Hashing feature transformation using Totally Random Trees
Hashing feature transformation using Totally Random Trees
![Plot class probabilities calculated by the VotingClassifier](../../_images/sphx_glr_plot_voting_probas_thumb.png)
Plot class probabilities calculated by the VotingClassifier
Plot class probabilities calculated by the VotingClassifier
![Plot the decision boundaries of a VotingClassifier](../../_images/sphx_glr_plot_voting_decision_regions_thumb.png)
Plot the decision boundaries of a VotingClassifier
Plot the decision boundaries of a VotingClassifier
![Plot the decision surfaces of ensembles of trees on the iris dataset](../../_images/sphx_glr_plot_forest_iris_thumb.png)
Plot the decision surfaces of ensembles of trees on the iris dataset
Plot the decision surfaces of ensembles of trees on the iris dataset
![Prediction Intervals for Gradient Boosting Regression](../../_images/sphx_glr_plot_gradient_boosting_quantile_thumb.png)
Prediction Intervals for Gradient Boosting Regression
Prediction Intervals for Gradient Boosting Regression
![Single estimator versus bagging: bias-variance decomposition](../../_images/sphx_glr_plot_bias_variance_thumb.png)
Single estimator versus bagging: bias-variance decomposition
Single estimator versus bagging: bias-variance decomposition