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
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