Computation times¶
00:50.770 total execution time for auto_examples_ensemble files:
Discrete versus Real AdaBoost ( |
00:13.300 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:07.052 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:05.424 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.046 |
0.0 MB |
Gradient Boosting regularization ( |
00:03.474 |
0.0 MB |
OOB Errors for Random Forests ( |
00:02.817 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:02.556 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.540 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.475 |
0.0 MB |
Feature importances with a forest of trees ( |
00:00.803 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.799 |
0.0 MB |
Gradient Boosting regression ( |
00:00.796 |
0.0 MB |
Monotonic Constraints ( |
00:00.745 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.661 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.407 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.398 |
0.0 MB |
Two-class AdaBoost ( |
00:00.361 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.297 |
0.0 MB |
IsolationForest example ( |
00:00.295 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.286 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.236 |
0.0 MB |
Combine predictors using stacking ( |
00:00.002 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.002 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.001 |
0.0 MB |