Computation times¶
02:09.064 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
00:49.449 |
0.0 MB |
Gradient Boosting regularization ( |
00:21.607 |
0.0 MB |
OOB Errors for Random Forests ( |
00:17.014 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:11.689 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:07.312 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:04.893 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:03.890 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.698 |
0.0 MB |
Two-class AdaBoost ( |
00:02.305 |
0.0 MB |
Gradient Boosting regression ( |
00:01.629 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.207 |
0.0 MB |
Monotonic Constraints ( |
00:00.881 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.736 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:00.634 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.541 |
0.0 MB |
IsolationForest example ( |
00:00.490 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.452 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.449 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.446 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.384 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.347 |
0.0 MB |
Combine predictors using stacking ( |
00:00.008 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.003 |
0.0 MB |