Computation times¶
02:34.584 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
01:01.955 |
0.0 MB |
Gradient Boosting regularization ( |
00:26.762 |
0.0 MB |
OOB Errors for Random Forests ( |
00:19.823 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:13.937 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:07.163 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:05.669 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:04.032 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:03.152 |
0.0 MB |
Two-class AdaBoost ( |
00:02.675 |
0.0 MB |
Gradient Boosting regression ( |
00:01.909 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.334 |
0.0 MB |
Monotonic Constraints ( |
00:01.152 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.931 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:00.655 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.600 |
0.0 MB |
IsolationForest example ( |
00:00.528 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.508 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.503 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.478 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.425 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.382 |
0.0 MB |
Combine predictors using stacking ( |
00:00.007 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.004 |
0.0 MB |