Computation times¶
03:19.548 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
01:12.248 |
0.0 MB |
Gradient Boosting regularization ( |
00:32.639 |
0.0 MB |
OOB Errors for Random Forests ( |
00:28.510 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:17.287 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:11.641 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:08.173 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:06.949 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:04.092 |
0.0 MB |
Two-class AdaBoost ( |
00:03.973 |
0.0 MB |
Gradient Boosting regression ( |
00:02.616 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:02.063 |
0.0 MB |
Monotonic Constraints ( |
00:01.733 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:01.189 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:01.088 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.916 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.851 |
0.0 MB |
IsolationForest example ( |
00:00.809 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.760 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.732 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.657 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.607 |
0.0 MB |
Combine predictors using stacking ( |
00:00.011 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.005 |
0.0 MB |