Computation times¶
03:35.688 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
01:01.613 |
0.0 MB |
Combine predictors using stacking ( |
00:34.063 |
0.0 MB |
Gradient Boosting regularization ( |
00:28.254 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:22.461 |
0.0 MB |
OOB Errors for Random Forests ( |
00:17.907 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:14.031 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:08.367 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:06.192 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:04.190 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:03.960 |
0.0 MB |
Two-class AdaBoost ( |
00:02.942 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:01.651 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.488 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:01.362 |
0.0 MB |
Gradient Boosting regression ( |
00:01.243 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:01.014 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.986 |
0.0 MB |
Monotonic Constraints ( |
00:00.848 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.623 |
0.0 MB |
IsolationForest example ( |
00:00.561 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.538 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.513 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.482 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.399 |
0.0 MB |