Computation times¶
01:24.085 total execution time for auto_examples_ensemble files:
Combine predictors using stacking ( |
00:18.317 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:12.721 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:11.462 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:08.406 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.822 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:05.283 |
0.0 MB |
Gradient Boosting regularization ( |
00:03.429 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.102 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:03.049 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.458 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.364 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:01.181 |
0.0 MB |
Feature importances with a forest of trees ( |
00:00.925 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.908 |
0.0 MB |
Gradient Boosting regression ( |
00:00.848 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.655 |
0.0 MB |
Monotonic Constraints ( |
00:00.537 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.458 |
0.0 MB |
Two-class AdaBoost ( |
00:00.448 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.429 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.344 |
0.0 MB |
IsolationForest example ( |
00:00.337 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.333 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.268 |
0.0 MB |