Computation times¶
01:01.330 total execution time for auto_examples_ensemble files:
Discrete versus Real AdaBoost ( |
00:15.026 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:08.530 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:06.599 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:06.313 |
0.0 MB |
Gradient Boosting regularization ( |
00:04.298 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.395 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:03.148 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:03.069 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.914 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.085 |
0.0 MB |
Monotonic Constraints ( |
00:01.055 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.049 |
0.0 MB |
Gradient Boosting regression ( |
00:01.047 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.905 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.546 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.475 |
0.0 MB |
Two-class AdaBoost ( |
00:00.442 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.373 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.364 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.352 |
0.0 MB |
IsolationForest example ( |
00:00.341 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.002 |
0.0 MB |
Combine predictors using stacking ( |
00:00.001 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.001 |
0.0 MB |