Computation times¶
02:59.655 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
01:05.847 |
0.0 MB |
OOB Errors for Random Forests ( |
00:27.296 |
0.0 MB |
Gradient Boosting regularization ( |
00:26.703 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:16.148 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:10.646 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:07.547 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:06.452 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:03.822 |
0.0 MB |
Two-class AdaBoost ( |
00:02.946 |
0.0 MB |
Gradient Boosting regression ( |
00:02.388 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.954 |
0.0 MB |
Monotonic Constraints ( |
00:01.409 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:01.108 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:00.834 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.736 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.707 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.691 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.640 |
0.0 MB |
IsolationForest example ( |
00:00.619 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.585 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.563 |
0.0 MB |
Combine predictors using stacking ( |
00:00.009 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.005 |
0.0 MB |