Computation times¶
01:03.944 total execution time for auto_examples_ensemble files:
Discrete versus Real AdaBoost ( |
00:13.858 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:13.701 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:06.220 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:05.756 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:03.797 |
0.0 MB |
Gradient Boosting regularization ( |
00:03.575 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.210 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.895 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.616 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.297 |
0.0 MB |
Monotonic Constraints ( |
00:01.022 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.939 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.873 |
0.0 MB |
Gradient Boosting regression ( |
00:00.831 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.716 |
0.0 MB |
IsolationForest example ( |
00:00.638 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.469 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.455 |
0.0 MB |
Two-class AdaBoost ( |
00:00.423 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.356 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.292 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.002 |
0.0 MB |
Combine predictors using stacking ( |
00:00.002 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.002 |
0.0 MB |