• Bagging 涉及在同一数据集的不同样本上拟合许多决策树并对预测进行平均。
    • Bagged Decision Trees (canonical bagging)
    • Random Forest
    • Extra Trees

    image.png

    • stacking堆叠涉及在相同数据上拟合许多不同的模型类型，并使用另一个模型来学习如何最好地组合预测。

    • Stacked Models (canonical stacking)
    • Blending
    • Super Ensemble
    • image.png
    • boosting提升涉及顺序添加集成成员，以纠正先前模型所做的预测并输出预测的加权平均值
    • AdaBoost (canonical boosting)
    • Gradient Boosting Machines
    • Stochastic Gradient Boosting (XGBoost and similar)

    image.png

    https://machinelearningmastery.com/category/ensemble-learning/page/2/
    https://machinelearningmastery.com/tour-of-ensemble-learning-algorithms/
    https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/