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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 regression

Gradient Boosting regularization

Hashing feature transformation using Totally Random Trees

IsolationForest example

Monotonic Constraints

Multi-class AdaBoosted Decision Trees

OOB Errors for Random Forests

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

Two-class AdaBoost

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https://scikit-learn.org/1.6/auto_examples/ensemble/index.html