Computation times¶

02:29.127 total execution time for auto_examples_ensemble files:

Early stopping of Gradient Boosting (`plot_gradient_boosting_early_stopping.py`)	00:54.698	0.0 MB
Gradient Boosting regularization (`plot_gradient_boosting_regularization.py`)	00:23.386	0.0 MB
OOB Errors for Random Forests (`plot_ensemble_oob.py`)	00:21.444	0.0 MB
Multi-class AdaBoosted Decision Trees (`plot_adaboost_multiclass.py`)	00:12.947	0.0 MB
Plot the decision surfaces of ensembles of trees on the iris dataset (`plot_forest_iris.py`)	00:09.177	0.0 MB
Discrete versus Real AdaBoost (`plot_adaboost_hastie_10_2.py`)	00:06.074	0.0 MB
Gradient Boosting Out-of-Bag estimates (`plot_gradient_boosting_oob.py`)	00:05.156	0.0 MB
Feature transformations with ensembles of trees (`plot_feature_transformation.py`)	00:03.013	0.0 MB
Two-class AdaBoost (`plot_adaboost_twoclass.py`)	00:02.530	0.0 MB
Gradient Boosting regression (`plot_gradient_boosting_regression.py`)	00:01.1000	0.0 MB
Single estimator versus bagging: bias-variance decomposition (`plot_bias_variance.py`)	00:01.490	0.0 MB
Monotonic Constraints (`plot_monotonic_constraints.py`)	00:01.271	0.0 MB
Plot individual and voting regression predictions (`plot_voting_regressor.py`)	00:01.095	0.0 MB
Prediction Intervals for Gradient Boosting Regression (`plot_gradient_boosting_quantile.py`)	00:00.785	0.0 MB
Decision Tree Regression with AdaBoost (`plot_adaboost_regression.py`)	00:00.681	0.0 MB
Feature importances with forests of trees (`plot_forest_importances.py`)	00:00.676	0.0 MB
Comparing random forests and the multi-output meta estimator (`plot_random_forest_regression_multioutput.py`)	00:00.650	0.0 MB
Plot the decision boundaries of a VotingClassifier (`plot_voting_decision_regions.py`)	00:00.620	0.0 MB
IsolationForest example (`plot_isolation_forest.py`)	00:00.579	0.0 MB
Plot class probabilities calculated by the VotingClassifier (`plot_voting_probas.py`)	00:00.429	0.0 MB
Hashing feature transformation using Totally Random Trees (`plot_random_forest_embedding.py`)	00:00.415	0.0 MB
Combine predictors using stacking (`plot_stack_predictors.py`)	00:00.008	0.0 MB
Pixel importances with a parallel forest of trees (`plot_forest_importances_faces.py`)	00:00.004	0.0 MB