Computation times¶
00:57.334 total execution time for auto_examples_ensemble files:
Prediction Intervals for Gradient Boosting Regression ( |
00:15.325 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:08.849 |
0.0 MB |
Gradient Boosting regularization ( |
00:07.929 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.692 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:04.507 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.667 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.947 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.310 |
0.0 MB |
Gradient Boosting regression ( |
00:01.219 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.067 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.917 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.713 |
0.0 MB |
Two-class AdaBoost ( |
00:00.596 |
0.0 MB |
Monotonic Constraints ( |
00:00.575 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.528 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.455 |
0.0 MB |
IsolationForest example ( |
00:00.421 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.319 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.291 |
0.0 MB |
Comparing Random Forests and Histogram Gradient Boosting models ( |
00:00.002 |
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 |
Early stopping in Gradient Boosting ( |
00:00.002 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.002 |
0.0 MB |