Computation times¶
02:35.898 total execution time for auto_examples_ensemble files:
Early stopping of Gradient Boosting ( |
00:41.762 |
0.0 MB |
Prediction Intervals for Gradient Boosting Regression ( |
00:36.519 |
0.0 MB |
Gradient Boosting regularization ( |
00:22.920 |
0.0 MB |
OOB Errors for Random Forests ( |
00:13.982 |
0.0 MB |
Multi-class AdaBoosted Decision Trees ( |
00:11.261 |
0.0 MB |
Plot the decision surfaces of ensembles of trees on the iris dataset ( |
00:05.613 |
0.0 MB |
Monotonic Constraints ( |
00:04.505 |
0.0 MB |
Discrete versus Real AdaBoost ( |
00:04.356 |
0.0 MB |
Feature transformations with ensembles of trees ( |
00:02.968 |
0.0 MB |
Gradient Boosting Out-of-Bag estimates ( |
00:02.597 |
0.0 MB |
Two-class AdaBoost ( |
00:02.044 |
0.0 MB |
Feature importances with a forest of trees ( |
00:01.742 |
0.0 MB |
Plot individual and voting regression predictions ( |
00:01.096 |
0.0 MB |
Gradient Boosting regression ( |
00:01.089 |
0.0 MB |
Single estimator versus bagging: bias-variance decomposition ( |
00:00.900 |
0.0 MB |
Decision Tree Regression with AdaBoost ( |
00:00.602 |
0.0 MB |
Plot the decision boundaries of a VotingClassifier ( |
00:00.532 |
0.0 MB |
Comparing random forests and the multi-output meta estimator ( |
00:00.449 |
0.0 MB |
IsolationForest example ( |
00:00.337 |
0.0 MB |
Hashing feature transformation using Totally Random Trees ( |
00:00.325 |
0.0 MB |
Plot class probabilities calculated by the VotingClassifier ( |
00:00.280 |
0.0 MB |
Combine predictors using stacking ( |
00:00.009 |
0.0 MB |
Categorical Feature Support in Gradient Boosting ( |
00:00.005 |
0.0 MB |
Pixel importances with a parallel forest of trees ( |
00:00.004 |
0.0 MB |