Computation times¶
03:33.695 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
01:06.502 |
0.0 MB |
Monotonic Constraints ( |
00:43.579 |
0.0 MB |
Gradient Boosting regularization ( |
00:29.309 |
0.0 MB |
OOB Errors for Random Forests ( |
00:22.485 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:15.892 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:08.177 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:06.961 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:04.871 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:03.670 |
0.0 MB |
Two-class AdaBoost ( |
00:02.799 |
0.0 MB |
Gradient Boosting regression ( |
00:02.036 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.577 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:01.015 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:00.790 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.706 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.615 |
0.0 MB |
IsolationForest example ( |
00:00.599 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.592 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.524 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.469 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.459 |
0.0 MB |
Combine predictors using stacking ( |
00:00.063 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.004 |
0.0 MB |