Computation times¶
00:59.797 total execution time for auto_examples_ensemble files:
Discrete versus Real AdaBoost ( |
00:13.984 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:13.513 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:05.760 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.049 |
0.0 MB |
Gradient Boosting regularization ( |
00:03.569 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.022 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.881 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:02.807 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.540 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.196 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.859 |
0.0 MB |
Gradient Boosting regression ( |
00:00.814 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.677 |
0.0 MB |
Monotonic Constraints ( |
00:00.561 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.478 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.446 |
0.0 MB |
Two-class AdaBoost ( |
00:00.412 |
0.0 MB |
IsolationForest example ( |
00:00.332 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.326 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.305 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.260 |
0.0 MB |
Combine predictors using stacking ( |
00:00.001 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.001 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.001 |
0.0 MB |