Computation times¶
02:23.805 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
00:34.860 |
0.0 MB |
OOB Errors for Random Forests ( |
00:22.449 |
0.0 MB |
Combine predictors using stacking ( |
00:21.331 |
0.0 MB |
Gradient Boosting regularization ( |
00:20.126 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:12.594 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:08.827 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:05.972 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:05.344 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.954 |
0.0 MB |
Two-class AdaBoost ( |
00:02.389 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.340 |
0.0 MB |
Gradient Boosting regression ( |
00:00.860 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:00.846 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.648 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.611 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.541 |
0.0 MB |
IsolationForest example ( |
00:00.538 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.509 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.436 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.413 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.211 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.004 |
0.0 MB |