Computation times¶
01:20.637 total execution time for auto_examples_ensemble files:
Comparing Random Forests and Histogram Gradient Boosting models ( |
00:15.546 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:12.503 |
0.0 MB |
Combine predictors using stacking ( |
00:08.005 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:06.952 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:05.935 |
0.0 MB |
Gradient Boosting regularization ( |
00:05.118 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:05.108 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:03.355 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:02.969 |
0.0 MB |
OOB Errors for Random Forests ( |
00:02.572 |
0.0 MB |
Early stopping of Gradient Boosting ( |
00:02.521 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.423 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.438 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:01.207 |
0.0 MB |
Gradient Boosting regression ( |
00:00.862 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.751 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:00.602 |
0.0 MB |
Monotonic Constraints ( |
00:00.516 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.461 |
0.0 MB |
Two-class AdaBoost ( |
00:00.398 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.343 |
0.0 MB |
IsolationForest example ( |
00:00.290 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.285 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.259 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.220 |
0.0 MB |