Computation times¶
02:47.629 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
00:51.289 |
0.0 MB |
Monotonic Constraints ( |
00:29.570 |
0.0 MB |
Gradient Boosting regularization ( |
00:22.329 |
0.0 MB |
OOB Errors for Random Forests ( |
00:19.155 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:12.315 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:08.261 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:05.630 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:04.639 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.934 |
0.0 MB |
Two-class AdaBoost ( |
00:02.530 |
0.0 MB |
Gradient Boosting regression ( |
00:02.416 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.407 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.922 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:00.707 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.609 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.561 |
0.0 MB |
IsolationForest example ( |
00:00.517 |
0.0 MB |
Feature importances with forests of trees ( |
00:00.496 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.496 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.436 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.399 |
0.0 MB |
Combine predictors using stacking ( |
00:00.008 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.004 |
0.0 MB |