Ensemble methods#
Examples concerning the sklearn.ensemble
module.
Categorical Feature Support in Gradient Boosting
Combine predictors using stacking
Comparing Random Forests and Histogram Gradient Boosting models
Comparing random forests and the multi-output meta estimator
Decision Tree Regression with AdaBoost
Early stopping in Gradient Boosting
Feature importances with a forest of trees
Feature transformations with ensembles of trees
Features in Histogram Gradient Boosting Trees
Gradient Boosting Out-of-Bag estimates
Gradient Boosting regularization
Hashing feature transformation using Totally Random Trees
Multi-class AdaBoosted Decision Trees
Pixel importances with a parallel forest of trees
Plot class probabilities calculated by the VotingClassifier
Plot individual and voting regression predictions
Plot the decision boundaries of a VotingClassifier
Plot the decision surfaces of ensembles of trees on the iris dataset
Prediction Intervals for Gradient Boosting Regression
Single estimator versus bagging: bias-variance decomposition