Computation timesΒΆ
16:27.317 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting (plot_gradient_boosting_early_stopping.py) |
03:58.169 | 0.0 MB |
OOB Errors for Random Forests (plot_ensemble_oob.py) |
03:06.745 | 0.0 MB |
Gradient Boosting regularization (plot_gradient_boosting_regularization.py) |
02:09.289 | 0.0 MB |
Multi-class AdaBoosted Decision Trees (plot_adaboost_multiclass.py) |
01:48.059 | 0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset (plot_forest_iris.py) |
01:33.946 | 0.0 MB |
Discrete versus Real AdaBoost (plot_adaboost_hastie_10_2.py) |
00:56.091 | 0.0 MB |
Two-class AdaBoost (plot_adaboost_twoclass.py) |
00:46.923 | 0.0 MB |
Gradient Boosting Out-of-Bag estimates (plot_gradient_boosting_oob.py) |
00:41.273 | 0.0 MB |
Feature transformations with ensembles of trees (plot_feature_transformation.py) |
00:29.938 | 0.0 MB |
Single estimator versus bagging: bias-variance decomposition (plot_bias_variance.py) |
00:12.452 | 0.0 MB |
Prediction Intervals for Gradient Boosting Regression (plot_gradient_boosting_quantile.py) |
00:06.545 | 0.0 MB |
Comparing random forests and the multi-output meta estimator (plot_random_forest_regression_multioutput.py) |
00:05.499 | 0.0 MB |
Gradient Boosting regression (plot_gradient_boosting_regression.py) |
00:05.362 | 0.0 MB |
Plot the decision boundaries of a VotingClassifier (plot_voting_decision_regions.py) |
00:05.278 | 0.0 MB |
Hashing feature transformation using Totally Random Trees (plot_random_forest_embedding.py) |
00:05.107 | 0.0 MB |
Feature importances with forests of trees (plot_forest_importances.py) |
00:04.966 | 0.0 MB |
Decision Tree Regression with AdaBoost (plot_adaboost_regression.py) |
00:04.117 | 0.0 MB |
IsolationForest example (plot_isolation_forest.py) |
00:03.895 | 0.0 MB |
Plot class probabilities calculated by the VotingClassifier (plot_voting_probas.py) |
00:03.609 | 0.0 MB |
Partial Dependence Plots (plot_partial_dependence.py) |
00:00.032 | 0.0 MB |
Pixel importances with a parallel forest of trees (plot_forest_importances_faces.py) |
00:00.023 | 0.0 MB |