Computation times¶
01:22.343 total execution time for auto_examples_ensemble files:
Prediction Intervals for Gradient Boosting Regression ( |
00:19.023 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:15.410 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:08.384 |
0.0 MB |
Gradient Boosting regularization ( |
00:07.242 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:07.037 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:06.464 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.848 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:03.225 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.903 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.592 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.153 |
0.0 MB |
Gradient Boosting regression ( |
00:01.118 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.849 |
0.0 MB |
IsolationForest example ( |
00:00.706 |
0.0 MB |
Monotonic Constraints ( |
00:00.604 |
0.0 MB |
Two-class AdaBoost ( |
00:00.566 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.558 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.494 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.462 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.364 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.339 |
0.0 MB |
Combine predictors using stacking ( |
00:00.002 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.001 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.001 |
0.0 MB |