Computation times

02:00.322 total execution time for auto_examples_ensemble files:

Early stopping of Gradient Boosting (plot_gradient_boosting_early_stopping.py)

00:52.524

0.0 MB

Gradient Boosting regularization (plot_gradient_boosting_regularization.py)

00:21.870

0.0 MB

OOB Errors for Random Forests (plot_ensemble_oob.py)

00:12.843

0.0 MB

Multi-class AdaBoosted Decision Trees (plot_adaboost_multiclass.py)

00:11.056

0.0 MB

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

00:05.109

0.0 MB

Discrete versus Real AdaBoost (plot_adaboost_hastie_10_2.py)

00:04.205

0.0 MB

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

00:02.603

0.0 MB

Feature transformations with ensembles of trees (plot_feature_transformation.py)

00:02.582

0.0 MB

Two-class AdaBoost (plot_adaboost_twoclass.py)

00:01.418

0.0 MB

Gradient Boosting regression (plot_gradient_boosting_regression.py)

00:01.326

0.0 MB

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

00:00.830

0.0 MB

Monotonic Constraints (plot_monotonic_constraints.py)

00:00.745

0.0 MB

Plot individual and voting regression predictions (plot_voting_regressor.py)

00:00.661

0.0 MB

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

00:00.396

0.0 MB

Prediction Intervals for Gradient Boosting Regression (plot_gradient_boosting_quantile.py)

00:00.354

0.0 MB

Feature importances with forests of trees (plot_forest_importances.py)

00:00.350

0.0 MB

IsolationForest example (plot_isolation_forest.py)

00:00.303

0.0 MB

Decision Tree Regression with AdaBoost (plot_adaboost_regression.py)

00:00.298

0.0 MB

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

00:00.283

0.0 MB

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

00:00.276

0.0 MB

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

00:00.245

0.0 MB

Combine predictors using stacking (plot_stack_predictors.py)

00:00.030

0.0 MB

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

00:00.013

0.0 MB