Computation times

01:01.037 total execution time for auto_examples_ensemble files:

Discrete versus Real AdaBoost (plot_adaboost_hastie_10_2.py)	00:13.874	0.0 MB
Prediction Intervals for Gradient Boosting Regression (plot_gradient_boosting_quantile.py)	00:10.276	0.0 MB
Plot the decision surfaces of ensembles of trees on the iris dataset (plot_forest_iris.py)	00:06.625	0.0 MB
Multi-class AdaBoosted Decision Trees (plot_adaboost_multiclass.py)	00:06.437	0.0 MB
Gradient Boosting regularization (plot_gradient_boosting_regularization.py)	00:03.606	0.0 MB
OOB Errors for Random Forests (plot_ensemble_oob.py)	00:03.267	0.0 MB
Feature transformations with ensembles of trees (plot_feature_transformation.py)	00:02.912	0.0 MB
Gradient Boosting Out-of-Bag estimates (plot_gradient_boosting_oob.py)	00:02.820	0.0 MB
Early stopping of Gradient Boosting (plot_gradient_boosting_early_stopping.py)	00:02.610	0.0 MB
Monotonic Constraints (plot_monotonic_constraints.py)	00:01.680	0.0 MB
Feature importances with a forest of trees (plot_forest_importances.py)	00:01.100	0.0 MB
Gradient Boosting regression (plot_gradient_boosting_regression.py)	00:00.988	0.0 MB
Single estimator versus bagging: bias-variance decomposition (plot_bias_variance.py)	00:00.957	0.0 MB
Plot individual and voting regression predictions (plot_voting_regressor.py)	00:00.719	0.0 MB
IsolationForest example (plot_isolation_forest.py)	00:00.641	0.0 MB
Hashing feature transformation using Totally Random Trees (plot_random_forest_embedding.py)	00:00.503	0.0 MB
Plot the decision boundaries of a VotingClassifier (plot_voting_decision_regions.py)	00:00.478	0.0 MB
Comparing random forests and the multi-output meta estimator (plot_random_forest_regression_multioutput.py)	00:00.471	0.0 MB
Two-class AdaBoost (plot_adaboost_twoclass.py)	00:00.422	0.0 MB
Decision Tree Regression with AdaBoost (plot_adaboost_regression.py)	00:00.352	0.0 MB
Plot class probabilities calculated by the VotingClassifier (plot_voting_probas.py)	00:00.292	0.0 MB
Categorical Feature Support in Gradient Boosting (plot_gradient_boosting_categorical.py)	00:00.002	0.0 MB
Combine predictors using stacking (plot_stack_predictors.py)	00:00.002	0.0 MB
Pixel importances with a parallel forest of trees (plot_forest_importances_faces.py)	00:00.002	0.0 MB