集成方法

有关的例子 sklearn.ensemble module.

梯度提升中的分类特征支持

Categorical Feature Support in Gradient Boosting

使用堆叠组合预测因子

Combine predictors using stacking

比较随机森林和柱状图梯度增强模型

Comparing Random Forests and Histogram Gradient Boosting models

比较随机森林和多输出Meta估计量

Comparing random forests and the multi-output meta estimator

使用AdaBoost进行决策树回归

Decision Tree Regression with AdaBoost

Gradient Boosting中的提前停止

Early stopping in Gradient Boosting

树木森林的重要性

Feature importances with a forest of trees

使用树木集合的特征转换

Feature transformations with ensembles of trees

梯度增强树的梯度中的功能

Features in Histogram Gradient Boosting Trees

梯度提升袋外估计

Gradient Boosting Out-of-Bag estimates

梯度增强回归

Gradient Boosting regression

梯度提升正规化

Gradient Boosting regularization

使用完全随机树哈希特征转换

Hashing feature transformation using Totally Random Trees

隔离森林示例

IsolationForest example

单调约束

Monotonic Constraints

多类AdaBoosted决策树

Multi-class AdaBoosted Decision Trees

随机森林的OOB错误

OOB Errors for Random Forests

绘制个人和投票回归预测图

Plot individual and voting regression predictions

在虹膜数据集中绘制树木集合的决策面

Plot the decision surfaces of ensembles of trees on the iris dataset

梯度Boosting回归的预测区间

Prediction Intervals for Gradient Boosting Regression

单一估计量与装袋：偏差方差分解

Single estimator versus bagging: bias-variance decomposition

两级AdaBoost

Two-class AdaBoost

可视化VotingClassifier的概率预测

Visualizing the probabilistic predictions of a VotingClassifier