Computation timesΒΆ
02:14.573 total execution time for auto_examples_ensemble files:
00:30.849: Early stopping of Gradient Boosting (
plot_gradient_boosting_early_stopping.py
)00:19.342: Multi-class AdaBoosted Decision Trees (
plot_adaboost_multiclass.py
)00:18.351: Gradient Boosting regularization (
plot_gradient_boosting_regularization.py
)00:14.377: Plot the decision surfaces of ensembles of trees on the iris dataset (
plot_forest_iris.py
)00:12.184: OOB Errors for Random Forests (
plot_ensemble_oob.py
)00:08.378: Discrete versus Real AdaBoost (
plot_adaboost_hastie_10_2.py
)00:06.917: Gradient Boosting Out-of-Bag estimates (
plot_gradient_boosting_oob.py
)00:05.169: Two-class AdaBoost (
plot_adaboost_twoclass.py
)00:04.542: Feature transformations with ensembles of trees (
plot_feature_transformation.py
)00:02.448: Pixel importances with a parallel forest of trees (
plot_forest_importances_faces.py
)00:02.127: Single estimator versus bagging: bias-variance decomposition (
plot_bias_variance.py
)00:01.278: Comparing random forests and the multi-output meta estimator (
plot_random_forest_regression_multioutput.py
)00:01.273: Prediction Intervals for Gradient Boosting Regression (
plot_gradient_boosting_quantile.py
)00:01.247: Gradient Boosting regression (
plot_gradient_boosting_regression.py
)00:01.235: Plot the decision boundaries of a VotingClassifier (
plot_voting_decision_regions.py
)00:01.185: Hashing feature transformation using Totally Random Trees (
plot_random_forest_embedding.py
)00:00.940: Decision Tree Regression with AdaBoost (
plot_adaboost_regression.py
)00:00.835: IsolationForest example (
plot_isolation_forest.py
)00:00.818: Feature importances with forests of trees (
plot_forest_importances.py
)00:00.659: Plot class probabilities calculated by the VotingClassifier (
plot_voting_probas.py
)00:00.419: Plot individual and voting regression predictions (
plot_voting_regressor.py
)