Computation times¶
02:37.508 total execution time for auto_examples_ensemble files:
Comparing Random Forests and Histogram Gradient Boosting models ( |
00:54.128 |
0.0 MB |
Combine predictors using stacking ( |
00:26.870 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:16.193 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:09.230 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:08.030 |
0.0 MB |
Gradient Boosting regularization ( |
00:07.572 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:06.383 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:04.333 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:04.128 |
0.0 MB |
OOB Errors for Random Forests ( |
00:03.935 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:03.794 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:03.136 |
0.0 MB |
Gradient Boosting regression ( |
00:01.448 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.206 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:01.195 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.014 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.930 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.669 |
0.0 MB |
Two-class AdaBoost ( |
00:00.658 |
0.0 MB |
Monotonic Constraints ( |
00:00.584 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.512 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.436 |
0.0 MB |
IsolationForest example ( |
00:00.428 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.373 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.322 |
0.0 MB |