Computation times

00:50.551 total execution time for auto_examples_ensemble files:

Prediction Intervals for Gradient Boosting Regression (plot_gradient_boosting_quantile.py)

00:14.043

0.0 MB

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

00:07.485

0.0 MB

Gradient Boosting regularization (plot_gradient_boosting_regularization.py)

00:07.073

0.0 MB

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

00:05.200

0.0 MB

Multi-class AdaBoosted Decision Trees (plot_adaboost_multiclass.py)

00:04.031

0.0 MB

OOB Errors for Random Forests (plot_ensemble_oob.py)

00:03.175

0.0 MB

Feature transformations with ensembles of trees (plot_feature_transformation.py)

00:02.676

0.0 MB

Gradient Boosting regression (plot_gradient_boosting_regression.py)

00:01.060

0.0 MB

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

00:00.909

0.0 MB

Plot individual and voting regression predictions (plot_voting_regressor.py)

00:00.836

0.0 MB

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

00:00.808

0.0 MB

Monotonic Constraints (plot_monotonic_constraints.py)

00:00.579

0.0 MB

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

00:00.567

0.0 MB

Two-class AdaBoost (plot_adaboost_twoclass.py)

00:00.445

0.0 MB

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

00:00.426

0.0 MB

Decision Tree Regression with AdaBoost (plot_adaboost_regression.py)

00:00.367

0.0 MB

IsolationForest example (plot_isolation_forest.py)

00:00.349

0.0 MB

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

00:00.262

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

00:00.248

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Combine predictors using stacking (plot_stack_predictors.py)

00:00.002

0.0 MB

Categorical Feature Support in Gradient Boosting (plot_gradient_boosting_categorical.py)

00:00.002

0.0 MB

Comparing Random Forests and Histogram Gradient Boosting models (plot_forest_hist_grad_boosting_comparison.py)

00:00.002

0.0 MB

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

00:00.002

0.0 MB

Early stopping in Gradient Boosting (plot_gradient_boosting_early_stopping.py)

00:00.002

0.0 MB