Computation times

03:03.509 total execution time for auto_examples_ensemble files:

Early stopping of Gradient Boosting (plot_gradient_boosting_early_stopping.py)

01:05.832

0.0 MB

Gradient Boosting regularization (plot_gradient_boosting_regularization.py)

00:29.186

0.0 MB

OOB Errors for Random Forests (plot_ensemble_oob.py)

00:28.011

0.0 MB

Multi-class AdaBoosted Decision Trees (plot_adaboost_multiclass.py)

00:16.194

0.0 MB

Plot the decision surfaces of ensembles of trees on the iris dataset (plot_forest_iris.py)

00:10.607

0.0 MB

Discrete versus Real AdaBoost (plot_adaboost_hastie_10_2.py)

00:07.668

0.0 MB

Gradient Boosting Out-of-Bag estimates (plot_gradient_boosting_oob.py)

00:06.384

0.0 MB

Feature transformations with ensembles of trees (plot_feature_transformation.py)

00:03.816

0.0 MB

Two-class AdaBoost (plot_adaboost_twoclass.py)

00:02.933

0.0 MB

Gradient Boosting regression (plot_gradient_boosting_regression.py)

00:02.351

0.0 MB

Single estimator versus bagging: bias-variance decomposition (plot_bias_variance.py)

00:01.985

0.0 MB

Monotonic Constraints (plot_monotonic_constraints.py)

00:01.455

0.0 MB

Plot individual and voting regression predictions (plot_voting_regressor.py)

00:01.117

0.0 MB

Prediction Intervals for Gradient Boosting Regression (plot_gradient_boosting_quantile.py)

00:00.985

0.0 MB

Comparing random forests and the multi-output meta estimator (plot_random_forest_regression_multioutput.py)

00:00.864

0.0 MB

Plot the decision boundaries of a VotingClassifier (plot_voting_decision_regions.py)

00:00.761

0.0 MB

IsolationForest example (plot_isolation_forest.py)

00:00.753

0.0 MB

Decision Tree Regression with AdaBoost (plot_adaboost_regression.py)

00:00.724

0.0 MB

Feature importances with forests of trees (plot_forest_importances.py)

00:00.702

0.0 MB

Hashing feature transformation using Totally Random Trees (plot_random_forest_embedding.py)

00:00.589

0.0 MB

Plot class probabilities calculated by the VotingClassifier (plot_voting_probas.py)

00:00.576

0.0 MB

Combine predictors using stacking (plot_stack_predictors.py)

00:00.012

0.0 MB

Pixel importances with a parallel forest of trees (plot_forest_importances_faces.py)

00:00.005

0.0 MB