Computation times¶
00:56.994 total execution time for auto_examples_ensemble files:
Discrete versus Real AdaBoost ( |
00:13.396 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:12.620 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:05.465 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.123 |
0.0 MB |
Gradient Boosting regularization ( |
00:03.498 |
0.0 MB |
OOB Errors for Random Forests ( |
00:02.883 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.647 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:02.601 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.500 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.151 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.820 |
0.0 MB |
Gradient Boosting regression ( |
00:00.809 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.661 |
0.0 MB |
Monotonic Constraints ( |
00:00.529 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.409 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.404 |
0.0 MB |
Two-class AdaBoost ( |
00:00.362 |
0.0 MB |
IsolationForest example ( |
00:00.299 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.294 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.275 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.242 |
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 |
Pixel importances with a parallel forest of trees ( |
00:00.001 |
0.0 MB |