Computation times¶
01:05.275 total execution time for auto_examples_ensemble files:
Prediction Intervals for Gradient Boosting Regression ( |
00:14.497 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:13.841 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:06.482 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.695 |
0.0 MB |
Gradient Boosting regularization ( |
00:04.536 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.927 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.880 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:02.765 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.586 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.282 |
0.0 MB |
Monotonic Constraints ( |
00:01.078 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:01.053 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.936 |
0.0 MB |
Gradient Boosting regression ( |
00:00.823 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.536 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.468 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.459 |
0.0 MB |
Two-class AdaBoost ( |
00:00.413 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.348 |
0.0 MB |
IsolationForest example ( |
00:00.342 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.322 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.003 |
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 |