Computation times¶
01:13.565 total execution time for auto_examples_ensemble files:
Discrete versus Real AdaBoost ( |
00:16.053 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:14.673 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:06.083 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:05.981 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.726 |
0.0 MB |
Gradient Boosting regularization ( |
00:05.680 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:04.703 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.280 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.941 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.260 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.995 |
0.0 MB |
Gradient Boosting regression ( |
00:00.978 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.904 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.770 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.738 |
0.0 MB |
Monotonic Constraints ( |
00:00.658 |
0.0 MB |
IsolationForest example ( |
00:00.561 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.482 |
0.0 MB |
Two-class AdaBoost ( |
00:00.463 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.314 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.314 |
0.0 MB |
Combine predictors using stacking ( |
00:00.003 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.003 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.002 |
0.0 MB |