Computation times

00:52.486 total execution time for auto_examples_ensemble files:

Discrete versus Real AdaBoost (plot_adaboost_hastie_10_2.py)

00:13.564

0.0 MB

Prediction Intervals for Gradient Boosting Regression (plot_gradient_boosting_quantile.py)

00:07.391

0.0 MB

Multi-class AdaBoosted Decision Trees (plot_adaboost_multiclass.py)

00:05.714

0.0 MB

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

00:05.327

0.0 MB

Gradient Boosting regularization (plot_gradient_boosting_regularization.py)

00:03.486

0.0 MB

OOB Errors for Random Forests (plot_ensemble_oob.py)

00:02.958

0.0 MB

Feature transformations with ensembles of trees (plot_feature_transformation.py)

00:02.672

0.0 MB

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

00:02.599

0.0 MB

Early stopping of Gradient Boosting (plot_gradient_boosting_early_stopping.py)

00:02.500

0.0 MB

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

00:00.827

0.0 MB

Feature importances with a forest of trees (plot_forest_importances.py)

00:00.818

0.0 MB

Gradient Boosting regression (plot_gradient_boosting_regression.py)

00:00.817

0.0 MB

Monotonic Constraints (plot_monotonic_constraints.py)

00:00.797

0.0 MB

Plot individual and voting regression predictions (plot_voting_regressor.py)

00:00.675

0.0 MB

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

00:00.423

0.0 MB

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

00:00.411

0.0 MB

Two-class AdaBoost (plot_adaboost_twoclass.py)

00:00.367

0.0 MB

Decision Tree Regression with AdaBoost (plot_adaboost_regression.py)

00:00.304

0.0 MB

IsolationForest example (plot_isolation_forest.py)

00:00.301

0.0 MB

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

00:00.285

0.0 MB

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

00:00.246

0.0 MB

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

00:00.002

0.0 MB

Combine predictors using stacking (plot_stack_predictors.py)

00:00.001

0.0 MB

Categorical Feature Support in Gradient Boosting (plot_gradient_boosting_categorical.py)

00:00.001

0.0 MB