Computation times¶
01:18.553 total execution time for auto_examples_ensemble files:
Prediction Intervals for Gradient Boosting Regression ( |
00:23.873 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:12.906 |
0.0 MB |
Gradient Boosting regularization ( |
00:10.819 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:07.223 |
0.0 MB |
OOB Errors for Random Forests ( |
00:05.169 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:05.126 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:03.368 |
0.0 MB |
Gradient Boosting regression ( |
00:01.519 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.409 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.330 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:01.157 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.803 |
0.0 MB |
Two-class AdaBoost ( |
00:00.735 |
0.0 MB |
Monotonic Constraints ( |
00:00.646 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.636 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.574 |
0.0 MB |
IsolationForest example ( |
00:00.522 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.366 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.362 |
0.0 MB |
Combine predictors using stacking ( |
00:00.002 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.002 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.002 |
0.0 MB |
Comparing Random Forests and Histogram Gradient Boosting models ( |
00:00.002 |
0.0 MB |
Early stopping in Gradient Boosting ( |
00:00.002 |
0.0 MB |