Computation times¶
04:06.009 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
01:29.758 |
0.0 MB |
Gradient Boosting regularization ( |
00:39.153 |
0.0 MB |
OOB Errors for Random Forests ( |
00:32.589 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:22.739 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:15.238 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:10.602 |
0.0 MB |
Two-class AdaBoost ( |
00:07.208 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:07.082 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:06.267 |
0.0 MB |
Gradient Boosting regression ( |
00:02.838 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:02.420 |
0.0 MB |
Monotonic Constraints ( |
00:01.735 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:01.380 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:01.034 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:01.027 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.880 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.877 |
0.0 MB |
IsolationForest example ( |
00:00.850 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.825 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.817 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.666 |
0.0 MB |
Combine predictors using stacking ( |
00:00.020 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.006 |
0.0 MB |