Computation times¶
02:44.611 total execution time for auto_examples_ensemble files:
Comparing Random Forests and Histogram Gradient Boosting models ( |
00:57.452 |
0.0 MB |
Combine predictors using stacking ( |
00:27.005 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:17.478 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:09.798 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:08.710 |
0.0 MB |
Gradient Boosting regularization ( |
00:07.908 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:06.725 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:04.327 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:04.060 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:03.932 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.879 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:03.239 |
0.0 MB |
Gradient Boosting regression ( |
00:01.435 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:01.344 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.192 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.069 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.963 |
0.0 MB |
Two-class AdaBoost ( |
00:00.720 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.675 |
0.0 MB |
Monotonic Constraints ( |
00:00.595 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.516 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.441 |
0.0 MB |
IsolationForest example ( |
00:00.431 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.384 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.334 |
0.0 MB |